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Climate change, environmental degradation, and limited resources are motivations for sustainable forest management. Forests, the most abundant renewable resource on earth, used to make a wide variety of forest-based products for human consumption. To provide a scientific measure of a product’s sustainability and environmental performance, the life cycle assessment (LCA) method is used. This article provides a comprehensive review of environmental performances of forest-based products including traditional building products, emerging (mass-timber) building products and nanomaterials using attributional LCA. Across the supply chain, the product manufacturing life-cycle stage tends to have the largest environmental impacts. However, forest management activities and logistics tend to have the greatest economic impact. In addition, environmental trade-offs exist when regulating emissions as indicated by the latest traditional wood building product LCAs. Interpretation of these LCA results can guide new product development using biomaterials, future (mass) building systems and policy-making on mitigating climate change. Key challenges include handling of uncertainties in the supply chain and complex interactions of environment, material conversion, resource use for product production and quantifying the emissions released.

This study explores the impact of oil depletion on the energetic efficiency of oil extraction and refining in California. These changes are measured using energy return ratios (such as the energy return on investment, or EROI). I construct a time-varying first-order process model of energy inputs and outputs of oil extraction. The model includes factors such as oil quality, reservoir depth, enhanced recovery techniques, and water cut. This model is populated with historical data for 306 California oil fields over a 50 year period. The model focuses on the effects of resource quality decline, while technical efficiencies are modeled simply. Results indicate that the energy intensity of oil extraction in California increased significantly from 1955 to 2005. This resulted in a decline in the life-cycle EROI from 6.5 to 3.5 (measured as megajoules (MJ) delivered to final consumers per MJ primary energy invested in energy extraction, transport, and refining). Most of this decline in energy returns is due to increasing need for steam-based thermal enhanced oil recovery, with secondary effects due to conventional resource depletion (e.g., increased water cut).

To meet international efforts to prevent climate change, the South Korean government is seeking to transform its main power source from coal to natural gas (NG), which emits less carbon dioxide (CO2) than coal. The government needs information about public preferences for increasing NG-fired generation. Therefore, the purpose of this paper is to provide the government with this information by gathering the data on public preferences for increasing NG-fired generation for reducing CO2 emissions, analyzing these data, and obtaining the implications from the analysis. To this end, a survey of 1000 people, after explaining the merits and demerits of NG-fired power, were asked to determine the proper ratio of NG-fired generation by choosing one of six examples: lower than 10%, 10%–20%, 20%–30%, 30%–40%, 40%–50%, and higher than 50%. An interval data model was employed to deal with the data. The average value of the ratio was estimated to be 26.7% with statistical significance, which is greater than the actual value of 22.2% from 2017. Finally, whether and how much some variables such as education level, age, gender, and income level of the respondent affected their preferences for NG-fired generation were investigated.

Plug-in electric vehicles (PEVs) have immense potential for reducing greenhouse gas emissions and dependence on fossil fuels, and for smart grid applications. Although a great deal of research is focused on technological limitations that affect PEV battery performance targets, a major and arguably equal concern is the constraint imposed by the finite availability of elements or resources used in the manufacture of PEV batteries. Availability of resources, such as lithium, for batteries is critical to the future of PEVs and is, therefore, a topic that needs attention. This study addresses the issues related to lithium availability and sustainability, particularly supply and demand related to PEVs and the impact on future PEV growth. In this paper, a detailed review of the research on lithium availability for PEV batteries is presented, key challenges are pinpointed and future impacts on PEV technology are outlined.

Methanesulfonic acid (MSA) is used to recover silver (Ag) from solar cells by adding an oxidizing agent. It is possible to regenerate by substituting of H+ for Ag+, and thus it can be reused for additional reactions. However, MSA is highly hygroscopic, and as an oxidizing agent can easily decompose in the acidic environment during Ag extraction, leading to dilution due to the formation of H2O. This H2O in the MSA solution hinders the Ag extraction. In this study, we present a fractional distillation process for restoring the reactivity of reused MSA solutions by reducing the H2O content. Our results showed that the reactivity of the separated MSA was restored and Ag could be recovered from the solar cell.

This work introduces statistical models for the energy harvested from the in-home hybrid power line-wireless channel in the frequency band from 0 to 100 MHz. Based on numerical analyses carried out over the data set obtained from a measurement campaign together with the use of the maximum likelihood value criterion and the adoption of five distinct power masks for power allocation, it is shown that the log-normal distribution yields the best model for the energies harvested from the free-of-noise received signal and from the additive noise in this setting. Additionally, the total harvested energy can be modeled as the sum of these two statistically independent random variables. Thus, it is shown that the energies harvested from this kind of hybrid channel is an easy-to-simulate phenomenon when carrying out research related to energy-efficient and self-sustainable networks.

The purpose of this paper is to recommend a break-even point for the capital investment cost for a Sodium-cooled Fast Reactor (SFR) when choosing between a Pyro-SFR nuclear fuel cycle (recycling option via Pyro-processing) and a direct disposal option. This is because the selection of an alternative cannot be justified without a guarantee of economic feasibility. The calculation of a break-even point is necessary because SFR capital investment cost makes up the largest share of the cost for electricity generation. In other words, the cost of capital investment is an important cost driver, and the one that exerts the greatest effect on Pyro-SFR nuclear fuel cycle economics. In the end, the break-even point of the SFR capital investment cost between the Pyro-SFR nuclear fuel cycle and the direct disposal was calculated to be 4284 US$/kWe. In other words, it is possible to claim that the potential for the economic viability of the Pyro-SFR nuclear fuel cycle is greater (compared to investing in direct disposal) when the SFR capital investment cost is 4284 US$/kWe or less. In addition, Pyro-SFR technology will enable sustainable nuclear power generation.

Because of the current increase in energy requirement, reduction in fossil fuels, and global warming, as well as pollution, a suitable and promising alternative to the non-renewable energy sources is proton exchange membrane fuel cells. Hence, the efficiency of the renewable energy source can be increased by extracting the precise values for each of the parameters of the renewable mathematical model. Various optimization algorithms have been proposed and developed in order to estimate the parameters of proton exchange membrane fuel cells. In this manuscript, a novel hybrid algorithm, i.e., Hybrid Particle Swarm Optimization Puffer Fish (HPSOPF), based on the Particle Swarm Optimization and Puffer Fish algorithms, was proposed to estimate the proton exchange membrane fuel cell parameters. The two models were taken for the parameter estimation of proton exchange membrane fuel cells, i.e., Ballard Mark V and Avista SR-12 model. Firstly, justification of the proposed algorithm was achieved by benchmarking it on 10 functions and then a comparison of the parameter estimation results obtained using the Hybrid Particle Swarm Optimization Puffer Fish algorithm was done with other meta-heuristic algorithms, i.e., Particle Swarm Optimization, Puffer Fish algorithm, Grey Wolf Optimization, Grey Wolf Optimization Cuckoo Search, and Particle Swarm Optimization Grey Wolf Optimization. The sum of the square error was used as an evaluation metric for the performance evaluation and efficiency of the proposed algorithm. The results obtained show that the value of the sum of square error was smallest in the case of the proposed HPSOPF, while for the Ballard Mark V model it was 6.621 × 10−9 and for the Avista SR-12 model it was 5.65 × 10−8. To check the superiority and robustness of the proposed algorithm computation time, voltage–current (V–I) curve, power–current (P–I) curve, convergence curve, different operating temperature conditions, and different pressure results were obtained. From these results, it is concluded that the Hybrid Particle Swarm Optimization Puffer Fish algorithm had a better performance in comparison with the other compared algorithms. Furthermore, a non-parametric test, i.e., the Friedman Ranking Test, was performed and the results demonstrate that the efficiency and robustness of the proposed hybrid algorithm was superior.