INVESTIGATE THE CARBON FOOTPRINTS OF THREE INTERMEDIATE FLOORING SYSTEMS: CROSS-LAMINATED TIMBER, SOLID CONCRETE, AND HOLLOW-CORE PRECAST CONCRETE

This paper evaluates and compares the embodied energy and embodied carbon using a Life Cycle Assessment (LCA) approach for three different intermediate floor structures, all of which use prefabricated materials—cross-laminated timber (CLT), precast hollow-core concrete, and solid concrete—to decide which floor construction materials have less environmental impact for use in the construction of a semi-detached house in the UK. The Inventory of Carbon & Energy (ICE) and the Carbon Calculator tool were used to calculate the carbon footprint from “cradle to grave” to determine whether the use of a CLT solution provides improved environmental performance over the traditional concrete solutions. The carbon footprint results indicate that the use of a hollow-core precast concrete floor system emits less carbon than the other two systems, although the concrete requires more fossil fuel input than the timber during the manufacturing process, so based on this, the footprint from cradle to gate for the timber was expected to be the less than that of the concrete. However, the results show the opposite; this is because of the differences in the material quantities needed in each system.

Energy-aware and Carbon-efficient VM Placement Optimization in Cloud Datacenters Using Evolutionary Computing Methods

One of the most important concerns of cloud service providers is balancing renewable and fossil energy consumption. On the other hand, the policy of organizations and governments is to reduce energy consumption and greenhouse gas emissions in cloud data centers. Recently, a lot of research has been conducted to optimize the Virtual Machine (VM) placement on physical machines to minimize energy consumption. Many previous studies have not considered the deadline and scheduling of IoT tasks. Therefore, the previous modelings are mainly not well-suited to the IoT environments where requests are time-constraint. Unfortunately, both the sub-problems of energy consumption minimization and scheduling fall into the category of NP-hard issues. In this study, we propose a multi-objective VM placement to joint minimizing energy costs and scheduling. After presenting a modified memetic algorithm, we compare its performance with baseline methods as well as state-of-the-art ones. The simulation results on the CloudSim platform show that the proposed method can reduce energy costs, carbon footprints, SLA violations, and the total response time of IoT requests.

Sustainable artificial intelligence: A corporate culture perspective

In recent years, various studies have highlighted the opportunities of artificial intelligence (AI) for our society. For example, AI solutions can help reduce pollution, waste, or carbon footprints. On the other hand, there are also risks associated with the use of AI, such as increasing inequality in society or high resource consumption for computing power. This paper explores the question how corporate culture influences the use of artificial intelligence in terms of sustainable development. This type of use includes a normative element and is referred to in the paper as sustainable artificial intelligence (SAI). Based on a bibliometric literature analysis, we identify features of a sustainability-oriented corporate culture. We offer six propositions examining the influence of specific manifestations on the handling of AI in the sense of SAI. Thus, if companies want to ensure that SAI is realized, corporate culture appears as an important indicator and influencing factor at the same time.

The Impact of Mixed-use Development, Small Businesses, and Walkability on Carbon Emissions in Cool Climate Cities

United States (US) cities of cool climate zone such as Chicago and Boston are witnessing a reduction in carbon emissions potentially due to focusing on public transportation, and alternative energy resources. It's difficult to validate or deny optimal practices and regulations due to a lack of reliable data on carbon emissions and urban comparative studies amongst metropolitan areas. Therefore, we have examined at the relationship between land use, walkability, socioeconomics variables and carbon dioxide emissions at the zip code level. The current study compares the carbon footprints of four metro regions in cool climatic zone 5 with a model of all US zip code, to generate a benchmarking predictive model for climate change across all US zip codes. Our research shows that increasing number of businesses within walkable distance in cool climate reduces CO2 emissions. This signifies that enhancing walkability in cities and remodeling of retail, art, entertainment, and recreation facilities in accordance with urban sustainability policies can greatly cut down CO2 emissions.

Assessment of Embodied Energy and Environmental Impact of Sustainable Building Materials and Technologies for Residential Sector

The energy demand of developing countries increases every year. Large amounts of energy are consumed during the production and transportation of construction materials. Conservation of energy became important in the perspective of limiting carbon emissions into the environment and for decreasing the cost of materials. This article is concentrated on some issues affecting the embodied energy of construction materials mainly in the residential sector. Energy consumption in three various wall structures has been made. The comparison demonstrated that the embodied energy of traditional wall structures is 3-times higher than the energy efficient building materials. CO2 emissions produced by conventional materials and green building materials are 54.96 Kg CO2/m2 and 35.33 Kg CO2/m2, respectively. Finally, the results revealed substantial difference in embodied energy and carbon footprints of materials for which its production involves a high amount of energy consumption.

Carbon Footprint Calculator Customized for Rice Products: Concept and Characterization of Rice Value Chains in Southeast Asia

In this study, we developed user-friendly software (CF-Rice) for calculating the carbon footprints (CF) of rice products. The approach follows the principles of Life Cycle Assessment while incorporating more flexibility for activities specific to rice production. The graphical user interface provides empirical emission and conversion factors obtained from the literature and from primary research studies of rice value chains. CF-Rice also allows the entering of new values for specific processes or practices. Data outputs distinguish among the contributions of individual stages of the value chain as well as different greenhouse gases (GHG), namely, CH4, N2O and CO2. The new tool was then applied to a scenario assessment of rice production in the regional context of Southeast Asia. The CF baseline of a typical rice value chain in the region accounted for a value of around 2300 g CO2e/kgProd. The CF can be reduced by about 27.4% through water-saving practices alone and can further be reduced up to 37.3% through interventions that increase product recovery rates and, thus, reduce food losses. In contrast, straw incorporation into the soil increased the CF by 26.0%. The tool is well suited for impact assessments of advanced practices and technologies of rice value chains.

Reliability aware green workflow scheduling using ε-fuzzy dominance in cloud

The enormous energy consumed by cloud data centers (CDCs) increases the carbon footprints, operational cost and decreases the system reliability, so it becomes a great challenge for CDCs providers. Dynamic voltage and frequency scaling (DVFS) is an efficient approach for energy efficiency, which reduces the operating frequency, and supply voltage of the processor during the task’s execution. Recent research shows that scaling of the supply voltage and operating frequency has negative impact on the system’s reliability as it increases transient fault rate of the resources. Thus, the system’s reliability and the energy consumption are two prime concerns in a cloud computing environment that requires attention. Most workflow scheduling algorithms in literature do not consider energy and reliability simultaneously. In this paper, we proposed the ε-fuzzy dominance based reliable green workflow scheduling (FDRGS) algorithm, which optimizes the application’s reliability and energy consumption simultaneously using the ε-fuzzy dominance mechanism. The simulation results obtained using fast Fourier transform (FFT) and gaussian elimination (GE) task graphs manifest that our scheduling algorithm is more efficient in optimizing energy consumption and lifetime system’s reliability jointly than several widely used algorithms. The proposed algorithm will help scientists and engineers for further insight into future research in the area of cloud.

Sustainable Management of Peanut Shell through Biochar and Its Application as Soil Ameliorant

The current research encompasses utilization of peanut shells (PS) as feedstock for pyrolysis carried out at various temperatures (250, 400, and 550 °C) for deriving biochar, namely PS-BC250, PS-BC400, and PS-BC550. After analyzing the biochar types physicochemically, it was applied as a soil ameliorant for the growth of cucumber. The results showed that in prepared biochar type, bulk density, volatile contents, hydrogen, oxygen, and nitrogen content decreased, whereas pH, electrical conductivity, ash content, fixed carbon content, and surface area increased with the increasing temperature. Scanning electron microscopy (SEM) and Fourier Transform Infrared Spectroscopy (FTIR) presented high porosity, re-orientation of vessels, and a greater number of aromatic compounds, respectively, for PS-BC prepared at 550 °C. On applying PS-BC250, PS-BC400, and PS-BC550 as amendments in potted soil at 2, 4, and 6% (w/w), it improved soil quality (viz pH, ECe, BD, and soil water holding capacity) and increased the yield of cucumber. Because of improved soil properties and crop yield, PS-BC550 at the rate of 4% (w/w) demonstrated a great potential for agricultural application while provisioning dual circular economic indicators in the form of diverting PS waste to an effective alternative of chemical fertilizer having intensive carbon footprints in cucumber production.