Decrease Carbon Emissions Research Articles

Facilitating winter wheat sustainable intensification: Effects of two limited carbon-emission cultivation patterns in China's Huang-Huai-Hai Region

As the growing population and the environment deterioration worldwide, it is a great challenge to explore how to use the available cropland and resource to feed more people with less environmental cost. Here, we performed a planting survey about winter wheat involving 1496 smallholders across the Huang-Huai-Hai Region. Based on this survey, we summarized the farmers' cultivation pattern (FP). By water and fertilizer coupling effects, we proposed a lower carbon-emission pattern (T1) and an equal carbon-emission pattern (T2). FP treatment included 270–330 kg ha−1 nitrogen (N), 120 kg ha−1 P2O5, 120 kg ha−1 K2O and irrigated once - three times; T1 treatment included 180–240 kg ha−1 N, 120 kg ha−1 P2O5, 80–120 kg ha−1 K2O, 0–15 kg ha−1 ZnSO4 and irrigated once - twice; T2 treatment included 240–300 kg ha−1 N, 120–180 kg ha−1 P2O5, 120–150 kg ha−1 K2O, 15–30 kg ha−1 ZnSO4 and irrigated once - three times. A 4-year cultivation experiment was conducted in 10 cities. The results showed that T1 and T2 treatments exhibited similar positive effects in promoting sustainable intensification in the region compared with FP. Compared with FP treatment, T1 and T2 increased the yields by 10.0% and 19.6%, water use efficiency by 19.8% and 15.2%, and partial factor productivity from applied N by 48.4% and 20.4% in the region, respectively; meanwhile, T1 treatment decreased carbon emissions by 25.2%. The yield of T1 and T2 treatments was higher than that of FP, mainly due to increased spike number, dry matter accumulation, and harvest index, which may be affected by the lower proportion of nitrogen fertilizer in the applied water and fertilizer. To facilitate sustainable intensification in the region, encouraging local farmers to adjust applied nutrients and water structures for lower carbon emissions will be a universal orientation. This research provides a reference for other similar regions.

Evaluating the impacts of imported cultural goods on carbon emission: Cross-country evidence

Low attention has been paid to the environmental effects of imported cultural commodities, which serve as a medium for cultural engagement and information sharing around the globe. Using the generalized method of moment (GMM) approach, this article empirically assesses the effects of imported cultural products on carbon emissions using cross-country panel data from 2004 to 2017 for 121 countries. The empirical findings reveal that the environmental consequences of imported cultural commodities vary among income level country groups. Overall, a 1% increase in the imports of total cultural goods imports would decrease carbon emissions significantly by 0.002%. After dissecting the sample into two groups, we find that a 1% increase in total cultural goods imports would lead to a significant decrease in carbon emissions of higher-income countries by 0.008%, but a significant increase in the emissions of carbon dioxide of lower-income countries by 0.002%, thus showing the importance of worldwide cultural interaction to carbon emissions. In addition, we find that performance & celebration goods and audiovisual & interactive media goods are primarily responsible for the inhibiting effects of imported cultural goods on the carbon emissions of higher-income countries. Imports of the majority of cultural products (with the exception of heritage items) would result in a significant rise in carbon emissions for low-income nations, a conclusion that is consistent with that of overall cultural goods imports. Gross domestic product per capita and economic complexity index have consistently negative effects on carbon emissions across several models for both nation groups, although energy consumption is the most significant contributor to carbon emissions. In addition, using Lasso Poisson regressions, we undertake robustness tests to assess the dependability of our empirical results based on the GMM methodology. The Lasso Poisson estimation results are consistent with our earlier findings.

Utilization of Ceramic Waste Material as Cement Substitution in Concrete

Researchers and engineers have been exploring alternative materials to partially substitute for cement in concrete to decrease carbon emissions and promote sustainability. Industrial pozzolanic waste is one such material that has gained attention as a potential substitute for cement. This research paper investigates the use of industrial pozzolanic waste materials, ceramic fine dust from the cyclones installed onto spray dryers in the process of manufacturing ceramic tiles, as a partial replacement for cement in concrete. Chemical analysis showed that ceramic dust could be used as pozzolana as ASTM C 618:2019. The study aims to evaluate the properties of fresh and hardened concrete; compressive strength; tensile splitting; and flexural strength of concrete mixtures. Additionally, specimens were subjected to durability performance: an acidic environment and water absorption tests. The cement was replaced in the concrete mix in proportions of 0, 5, 7, 10, 15, 20, and 30% by weight with a water-to-binder ratio of 0.45 at different curing periods. The results showed an increase in compressive strength of up to 10% replacement over time; fortunately, even with up to 30% replacement, it is still within the characteristic value of 30 MPa. The workability of the concrete decreases with replacement. The flexural tensile strength improved by 8% and the splitting strength by 5% with a replacement of 7% set against the control sample. The water absorption of concrete decreased by up to 10% replacement level, which was 15% less than that of the control mix. Concrete subjected to a 2% sulfuric acid solution (acid attack) revealed a loss in compressive strength when set against those not subjected to acid attacks. Based on the present research, ceramic dust as pozzolanic waste material can be used as a partial substitute for cement in the manufacture of good quality concrete under standard conditions.

Enhancing the engineering supervision process in China: A solution enabled by integrating hybrid blockchain system

This paper analyzes the current status of the engineering supervision process in construction and infrastructure projects in China. It discusses the existing problems, such as low efficiency of supervision, distorted monitoring data, resource waste, and environmental damage. Based on extensive literature research and combining the consensus mechanism, ECDSA encryption algorithm and so on, the paper proposes the idea of integrating blockchain technology with the engineering supervision process. This includes the decision of blockchain type, the use of digital signature technology to implement responsibility, and the construction of a hybrid blockchain system using the blockchain's consensus mechanism. The paper presents the general design, the design of the system structures, and the design of modules of the system. Through this system, project participants can verify their identities and share sensitive information confidentially, thereby improving the efficiency of supervision work and preventing data forgery, reducing resource waste, decreasing carbon emissions, and minimizing environmental damage.

Digitalization and CO2 emissions: Dynamics under R&D and technology innovation regimes

New digital technologies help to curtail those that are carbon intensive but are accompanied by CO2 emissions due to their own energy demand. Whether digitalization relates to increased or decreased carbon emissions may depend on the sophistication of an economy's research and development (R&D) output. This study explores the R&D-induced regime transition that governs the relationship between digitalization and CO2 emissions. The study seeks evidence of the environmental Kuznets curve hypothesis and tests it with a panel smooth transition regression (PSTR). Employing this estimator with two R&D output regimes makes it possible to account for year- and country-varying effects of digitalization, human capital, and country income level on CO2 emission. The research covers 55 high- and middle-income economies from 1996 to 2019. The paper finds that the transition process is driven by R&D output level – measured in technology patents per country inhabitants. The findings support the environmental Kuznets curve hypothesis and confirm that CO2 emissions have an inverted U-shaped relationship with digitalization and income level. This nonlinear relationship transitions smoothly in the exogenous R&D output level. The digitalization indicator in the lower R&D regime has a significant point estimate of 0.07; in the higher regime, the estimate is −0.14. The R&D output threshold at which the transition function switches between the two regimes corresponds to a level of 39.9 technology patents per million inhabitants.

Multi-Objective Adaptive Optimization Model Predictive Control: Decreasing Carbon Emissions from a Zinc Oxide Rotary Kiln

The zinc oxide rotary kiln, as an essential piece of equipment in the zinc smelting industrial process, is presenting new challenges in process control. China’s strategy of achieving a carbon peak and carbon neutrality is putting new demands on the industry, including green production and the use of fewer resources; thus, traditional stability control is no longer suitable for multi-objective control tasks. Although researchers have revealed the principle of the rotary kiln and set up computational fluid dynamics (CFD) simulation models to study its dynamics, these models cannot be directly applied to process control due to their high computational complexity. To address these issues, this paper proposes a multi-objective adaptive optimization model predictive control (MAO-MPC) method based on sparse identification. More specifically, with a large amount of data collected from a CFD model, a sparse regression problem is first formulated and solved to obtain a reduction model. Then, a two-layered control framework including real-time optimization (RTO) and model predictive control (MPC) is designed. In the RTO layer, an optimization problem with the goal of achieving optimal operation performance and the lowest possible resource consumption is set up. By solving the optimization problem in real time, a suitable setting value is sent to the MPC layer to ensure that the zinc oxide rotary kiln always functions in an optimal state. Our experiments show the strength and reliability of the proposed method, which reduces the usage of coal while maintaining high profits.

Energy and Exergy Analysis on Zeotropic Refrigerants R-455A and R-463A as Alternatives for R-744 in Automotive Air-Conditioning System (AACs)

The popularity of vehicles and the increased time spent in cars with air conditioning systems has led to regulations in many countries that require the use of environmentally friendly refrigerants with minimal global warming and zero ozone depletion potential (GWP and ODP). Cars need high-performance, eco-friendly air conditioning systems to reduce their impact on the environment, lower fuel consumption, and decrease carbon emissions. The aim of the current work was to propose CO2-based blend zeotropic refrigerants, R-455A (R-744/32/1234yf) and R-463A (R-744/32/125/1234yf/134a), to improve the thermodynamic performance of pure CO2 refrigerants. The thermodynamic energy and exergy analysis and system optimization of an AAC system for the new zeotropic refrigerant blends compared to carbon dioxide (R-744), using Aspen HYSYS software, were investigated. The influence of cooler/condenser pressure, average evaporator temperature, cooler/condenser outlet temperature, and refrigerant flow rate on the cycles’ COP and exergy efficiency were conducted and are presented. The results showed that, at the same operating condition parameters, the cycle COP improved by 57.6 and 76.5% when using R455A and R463A instated of R744, respectively, with the advantage of reducing leakage problems due to the higher operating pressure of R744 (5–7 times higher than those of R455A and R463A), as well as requiring heavy equipment, but at optimal operating condition parameters, R744 and R-463A had a maximum COP of 14.58 and 14.19, respectively. The maximum COPs of R744, R455A, and R463A based on the optimal pressure of the cooler/condenser were 3.1, 4.25, and 5.4, respectively. Additionally, regarding the need for environmentally friendly air conditioning systems with acceptable performance in cars due to their impact on the environment and their contribution to global warming, the blend R455A is recommended for use as a refrigerant in AAC systems.

Central inspections of environmental protection and transition for low-carbon Chinese cities: Policy intervention and mechanism analysis

The central government continues to elevate ecological considerations in local governments' incentive and penalty systems under the relentless pressure of dual carbon objectives. The central inspections of environmental protection (CIEP) program have emerged as an increasingly significant tool and institutional arrangement for environmental governance. Therefore, this study evaluates the impact of CIEP policy intervention on carbon emissions intensity employing the time-varying difference-in-differences model (TV-DID) in 233 Chinese cities from 2008 to 2019. The empirical evidence reveals that CIEP's enforcement significantly reduces carbon emissions intensity, and these effects are consistent for different model specifications. The mechanism analysis indicates that CIEP decreases carbon emissions intensity by improving energy utilization efficiency and intensifying local government environmental attention. The negative influence of CIEP on carbon emission intensity is only significant in eastern cities, high administrative hierarchy cities, and non-resource-based cities but insignificant in mid-western cities, ordinary cities, and resource-based cities. These findings support the CIEP implementation to achieve energy transition and carbon neutrality targets.

Achieving Synergies of Carbon Emission Reduction, Cost Savings, and Asset Investments in China’s Industrial Sector: Towards Sustainable Practices

This study aims to investigate the dynamic correlations among carbon emission reduction, total cost savings, and asset investments in the industrial sector in China. This study uses the panel vector autoregressive (PVAR) model and the generalized method of moments (GMM) model to obtain three conclusions based on Chinese industrial industry data from 2005–2019. (1) The interaction between carbon emission reduction and cost reduction is bidirectional. A carbon emission decrease can result in persistent cost cutting, while measures in shrinking costs lead to reducing carbon emissions with lasting effects. Moreover, carbon emission decline has strong inertia, while cost reduction is softer. (2) Green investment promotes reducing carbon emissions and is efficient and sustainable. Conversely, completing carbon reduction milestones will inhibit asset expansion in the subsequent period. (3) China’s industrial sector has already achieved the “synergy of emission reduction and cost decrease” development model. The transmission chain “asset investment–carbon emission decline–cost decrease–carbon emission abatement” has been established. Nonetheless, a gap remains between the mature cycle of decarbonization, cost saving, and effectiveness. Finally, it is recommended that the government focuses on the synergistic effect of carbon and cost reduction, encourages continuous green investment, and systematically organizes decarbonization actions. This study provides a basis for increasing the interest of companies in transitioning to a low-carbon economy, contributing to the simultaneous realization of green development and economic benefits.

A scheduling framework for VPP considering multiple uncertainties and flexible resources

This paper presents a two-stage scheduling framework for a virtual power plant (VPP) to address uncertainties in wind power plants (WPP), photovoltaic (PV), load, and price in the power system. The framework consists of a day-ahead (DA) scheduling stage and an intraday adjustment stage. The DA scheduling stage formulates the VPP's internal controllable resource scheduling strategy and the energy market's bidding plan. The intraday adjustment stage provides flexibility to adjust the scheduling strategy of controllable resources in the VPP to enhance power generation stability. To address profit distribution issues, the paper proposes a profit distribution strategy based on scheduling costs of each component unit of the VPP. The VPP considers various factors, such as battery capacity degradation, carbon credit trading, flexibility of electric vehicles (EVs), and novel demand response (DR) strategies. The case study demonstrates that accounting for battery capacity degradation reduces the utilization of energy storage systems (ESSs) and EVs, while considering controllable load and EVs increases the VPP's flexibility and decreases carbon emissions. Furthermore, the proposed two-stage scheduling strategy effectively handles multiple uncertainties. Finally, the implementation of new DR strategies leads to a reduction in cost by 6.83% (time-of-use) and 8.1% (real-time price) for the VPP.

Integrated Energy Station Optimal Dispatching Using a Novel Many-Objective Optimization Algorithm Based on Multiple Update Strategies

Regarding the need to decrease carbon emissions, the electric vehicle (EV) industry is growing rapidly in China; the charging needs of EVs require the number of EV charging stations to grow significantly. Therefore, many refueling stations have been modified to integrated energy stations, which contain photovoltaic systems. The key issue in current times is to figure out how to operate these integrated energy stations in an efficient way. Therefore, an effective scheduling model is needed to operate an integrated energy station. Photovoltaic (PV) and energy storage systems are integrated into EV charging stations to transform them into integrated energy stations (PE-IES). Considering the demand for EV charging during different time periods, the PV output, the loss rate of energy storage systems, the load status of regional grids, and the dynamic electricity prices, a multi-objective optimization scheduling model was established for operating integrated energy stations that are connected to a regional grid. The model aims to simultaneously maximize the daily profits of the PE-IES, minimize the daily loss rate of the energy storage system, and minimize the peak-to-valley difference of the load in the regional grid. To validate the effectiveness of the model, simulation experiments under three different scenarios for the PE-IES were conducted in this research. Each object weight was determined using the entropy weight method, and the optimal solution was selected from the Pareto solution set using an order-preference technique according to the similarity to an ideal solution (TOPSIS). The results demonstrate that, compared to traditional charging stations, the daily revenue of the PE-IES stations increases by 26.61%, and the peak-to-valley difference of the power load in the regional grid decreases by 30.54%, respectively. The effectiveness of PE-IES is therefore demonstrated. Furthermore, to solve the complex optimization problem for PE-IES, a novel multi-objective optimization algorithm based on multiple update strategies (MOMUS) was proposed in this paper. To evaluate the performance of the MOMUS, a detailed comparison with seven other algorithms was demonstrated. These results indicate that our algorithm exhibits an outstanding performance in solving this optimization problem, and that it is capable of generating high-quality optimal solutions.

The Impact of Green Finance on Carbon Emissions in China: An Energy Consumption Optimization Perspective

From the perspective of energy consumption optimization, this paper studies the impact of green finance on carbon emissions in China. Firstly, based on the theoretical perspective, this paper explores the mechanism and path by which green finance influences carbon emissions, and analyzes the role of energy consumption in this process. Then, this paper utilizes the STIRPAT model, chain multiple mediation effect model and panel threshold model to empirically analyze the influence of green finance on carbon emissions, using provincial data from China from 2005 to 2019. The results are as follows: (1) Green finance significantly reduces carbon emissions. After accounting for potential endogeneity, this conclusion is still valid. The heterogeneity test reveals that the inhibitory effect is more remarkable in northern regions, high-carbon emission regions and energy-rich regions. (2) The results of the bootstrap test reveal that at the national level, green finance decreases carbon emissions through three paths: green technological innovation, ecological evolution of the industrial structure and green technological innovation facilitating ecological evolution of the industrial structure. Furthermore, in energy-rich regions, green finance significantly inhibits carbon emissions through all three paths, while in energy-poor regions, green finance reduces carbon emissions only through green technological innovation. (3) There is a nonlinear relationship between green finance and carbon emissions. Specifically, regardless of energy intensity or energy consumption structure, only when it is below the threshold can green finance significantly inhibit carbon emissions. Thus, realizing energy consumption optimization is an effective way to ensure the carbon emission reduction effect of green finance.

Microstructure, phase evolution, mechanical properties and slag corrosion resistance of ZrO2 reinforced Al2O3–Cr2O3 composites

Al2O3–Cr2O3 refractories have excellent slag corrosion resistance and can adapt to the oxidation/reduction atmosphere in the smelting reduction ironmaking furnace. Improving the corrosion resistance of chrome corundum refractories is of great significance for extending the life length of industrial kilns and decreasing carbon emissions. In this work, the influences of ZrO2 content on the phase evolution, microstructure, physicochemical properties, mechanical properties and slag corrosion resistance of the ZrO2 reinforced Al2O3–Cr2O3 composite refractories were investigated. The results show that the ZrO2 reinforced Al2O3–Cr2O3 composites are primarily composed of (Al, Cr)2O3 solid solution and m-ZrO2. The compressive strength, flexural strength and hardness of the composite refractories can be improved by adding 2.5 wt% ZrO2. However, adding more ZrO2 will reduce its mechanical properties. This is due to ZrO2 excessive content will augement the porosity of the composite refractories, and the phase transformation of ZrO2 will also lead to the formation of microcracks during the cooling process. It is worth noting that higher ZrO2 content improves the thermal shock resistance of the composites, which is primarily put down to the microcrack toughening mechanism of ZrO2. The corrosion tests show that the thicknesses of the corrosion and infiltration layers of composite refractories are significantly decreased accompanied by ZrO2 content increase. This is because the formation of high melting point CaZr4O9 reduces the CaO content and slag alkalinity, and inhibits the dissolution of Al2O3 into the slag to a certain extent. Therefore, the slag corrosion/penetration resistance of the of the composites are obviously improved with the increase of ZrO2 addition.

Exploring the effect of human capital on carbon emissions: evidences from 125 countries.

Human capital (HC) plays a crucial role in economic growth, and also has a considerable effect on environmental performance, including carbon emissions (CEs). Existing studies have drawn inconsistent conclusions on whether and how HC affects CEs, and most of them conduct case studies of a certain country or several countries with similar economic backgrounds. In order to accurately determine the effect and the influence mechanism of HC on CEs, this research conducted an empirical study by applying econometric method and the panel data of 125 countries over the period 2000-2019. The empirical results indicate that there is an inverted U-shaped nexus between HC and CEs of full sample countries, revealing that HC will increase CEs before turning point and decrease CEs after turning point. From a heterogeneity perspective, this inverted U-shaped nexus only exists in high and upper-middle income countries, while is not supported in low and lower-middle income countries. This study further disclosed that HC can affect CEs by the mediating effects of labor productivity, energy intensity, and industrial structure from a macro perspective. Specifically, HC will increase CEs by promoting labor productivity, while decrease CEs by reducing energy intensity and the proportion of secondary industry. These results can provide important references for governments of different countries to make tailored carbon reduction policies according to the mitigation effect of HC on CEs.

Carbon emission-aware job scheduling for Kubernetes deployments

Decreasing carbon emissions of data centers while guaranteeing Quality of Service (QoS) is one of the major challenges for efficient resource management of large-scale cloud infrastructures and societal sustainability. Previous works in the area of carbon reduction mostly focus on decreasing overall energy consumption, replacing energy sources with renewable ones, and migrating workloads to locations where lower emissions are expected. These measures do not consider the energy mix of the power used for the data center. In other words, all KWh of energy are considered the same from the point of view of emissions, which is rarely the case in practice. In this paper, we overcome this deficit by proposing a novel practical CO2-aware workload scheduling algorithm implemented in the Kubernetes orchestrator to shift non-critical jobs in time. The proposed algorithm predicts future CO2 emissions by using historical data of energy generation, selects time-shiftable jobs, and creates job schedules utilizing greedy sub-optimal CO2 decisions. The proposed algorithm is implemented using Kubernetes’ scheduler extender solution due to its ease of deployment with little overheads. The algorithm is evaluated with real-world workload traces and compared to the default Kubernetes scheduling implementation on several actual scenarios.

Do financial development, financial stability and renewable energy disturb carbon emissions? Evidence from asia-pacific economic cooperation economics.

The present paper investigates the influence of renewable energy consumption (REC), economic growth (GDP), financial development index (FDI), z-score (ZS) and control of corruption (CC) on carbon dioxide (CO2) emissions, for eighteen different APEC economies over the period 2000-2019 using the Pooled Mean Group-Autoregressive Distributed Lags (PMG-ARDL) approach and Granger causality tests. The outcomes of the empirical study confirm that the variables are cointegration using Pedroni tests. The long-run estimates revealed that economic growth and renewable energy contribute to the huge of carbon emissions, while financial development, ZS and CC lead to decrease carbon emissions. Granger causality shows that, in the long-run, there is bidirectional causality between CO2 emissions, economic growth, and financial development. In the short-run and for basic variables, Granger shows a unidirectional causality from CO2 emissions and economic growth to REC and; unidirectional causality from financial development, ZC and CC to CO2 emissions. A comprehensive approach is needed in APEC countries to effectively reduce CO2 emissions and promote sustainable development, including encouraging green financial products, reinforcing financial regulations, transitioning to a low-carbon economy, enhancing renewable energy usage, and improving governance and institutional quality, while considering the distinctive characteristics of each country.

The role of energy, political stability, and real income on achieving carbon neutrality: asymmetric evidence.

Apart from business considerations stemming from the marketplace, businesses, individuals, and the economy at large, political decisions also play a role on environmental quality. Governments make a series of policies that impact private businesses, sectors, the environment, and the economy at large. In this paper, we test the asymmetric role of political risk on CO2 emissions, while controlling for renewable energy, non-renewable energy, and real income: policy toward environmental sustainability objectives in the context of Turkey. To realize the motive of this study, we capture the asymmetric effect of the regressors by adopting the nonlinear autoregressive distributed lag method (NARDL). This research adds to the environmental literature in terms of methodological and empirical. Methodologically, the study shows that a nonlinear relationship exists among the variables, and it has a significant impact on environmental sustainability targets. The outcome of the NARDL indicates that the increasing political risk, non-renewable energy, and economic growth follow a trajectory trend on carbon emissions, which is unsustainable in Turkey, but renewable energy is sustainable. Moreover, decreasing real income and non-renewable energy decreases carbon emissions. This research also deployed the frequency domain test to capture the causal association of the concerned variables and the outcome indicates political risk, renewable energy, non-renewable energy use, and real income are predictors of CO2 in Turkey. From this result, policies geared toward promoting a sustainable environment were formulated.

CO2 Emission Allocation for Urban Public Buildings Considering Efficiency and Equity: An Application at the Provincial Level in China

China is currently recognized as the leading global energy consumer and CO2 emitter. A significant amount of carbon emissions can be attributed to urban public buildings. Establishing an equitable and efficient carbon emission allocation mechanism is a crucial step to meeting the ambitious targets in China’s 2030 carbon peak plan. In this study, we estimate the total amount of CO2 emissions from urban public buildings by 2030 and propose a preliminary scheme of carbon quota assignment for each province. By means of applying the zero-sum gains data envelopment analysis (ZSG-DEA) model, the carbon emission quotas allocation of urban public buildings in China’s 30 provinces is proposed, and the corresponding pressure to reduce provincial carbon emissions is analyzed. The results indicate that Qinghai has the lowest carbon emission rate (0.01%) for urban public buildings, while Guangdong has the highest (9.06%). Among the provinces, Jiangsu, Jiangxi, and Tianjin face the least pressure in reducing carbon emissions from urban public buildings. On the other hand, Hebei, Beijing, and Anhui are under great pressure to decrease carbon emissions. Notably, Hebei is predicted to have the highest emission reduction requirement of 95.66 million tons. In terms of pressures on carbon emissions reduction for urban public buildings, Jiangsu, Jiangxi, and Tianjin exhibit the least pressure. Hebei, Beijing, and Anhui are facing intense pressure to decrease carbon emissions. These findings offer policymakers valuable insights into developing a fair and efficient carbon allowance allocation strategy, while also contributing to China’s efforts to mitigate carbon emissions and combat climate change.

Experimental investigation on the impact properties and microstructure of recycled steel fiber and silica fume reinforced recycled aggregate concrete

With the continuous development of the economy and society, the production of solid waste is increasing. Construction and demolition waste (CDW), used tires and silica fume are the main components of solid waste. Recycling these materials will reduce resource pressure, decrease carbon emissions and protect the environment, while bringing certain economic benefits. This study focused on the influence of the synergistic effect of recycled steel fibers (RSF) and silica fume on the mechanical properties and impact resistance of recycled aggregate concrete (RAC). The mathematical statistical model of Weibull distribution was adopted to depict the impact results. In addition, the microstructure of the interfacial transition zone (ITZ) between the cement paste and the aggregate/fiber was researched by scanning electron microscopy (SEM). Results revealed that the synergistic effect of RSF and silica fume significantly improved the impact resistance of RAC. Especially the specimen containing 10% silica fume and 0.75% RSF performed the best properties, the first crack impact and failure impact energy increased by 421.9% and 461.3%, the net-gain in them by the synergistic action of silica fume and RSF with values of 102.7% and 115.3%. The distribution law of the impact life for RSF reinforced RAC can better describe by the two-parameter Weibull distribution function. Impact resistance life was predicted corresponding to reliability levels. SEM investigations revealed that the ITZ between cement and aggregate/RSF significantly improved by mixing silica fume.

Significance of artificial intelligence in the development of sustainable transportation

The idea of Sustainable development has gained more attention since it involves satisfying needs without degrading the environment. Sustainable development has many targets across various subjects in which Sustainable Transportation is one of the cores to sustainable development. The objective of sustainable transportation is to achieve social, environmental and economic growth with universal access, enhanced safety, improved resilience and infrastructure, reduction of emission in greenhouse gases, traffic management, freight management by different modes of transportation. It is vital and interlinked across several sustainable development goals (SDGs) and targets in the 2030 Agenda proposed by United Nations. There is a need to improve sustainable transportation, which can be achieved through artificial intelligence as it helps the sector to increase public transport, traffic safety, reduce accidents and improve traffic management, decrease carbon emissions, rise reliability and also leverage economic growth through methods like Fuzzy logic (FL), Artificial Neural Networks, Ant Colony Optimiser, Genetic algorithms, Swarm optimization algorithm, Simulated Annealing. It is also supported by Big Data Analysis, Internet of Things, Robotic process Automation. This paper addresses the overview of the importance of sustainable transportation along with the role of artificial intelligence in different modes of transportation, current level of sustainability across globe, progress in transport system and conclude with challenges and future improvements that can be made towards the goal.