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The prevailing need for a more sustainable management of natural resources depends not only on the decisions made by governments and the will of the population, but also on the knowledge of the role of energy in our society and the relevance of preserving natural resources. In this sense, critical work is being done to instill key concepts—such as the circular economy and sustainable energy—in higher education institutions. In this way, it is expected that future professionals and managers will be aware of the importance of energy optimization, and will learn a series of computational methods that can support the decision-making process. In the context of higher education, this paper reviews the main trends and challenges related to the concepts of circular economy and sustainable energy. Besides, we analyze the role of simulation and serious games as a learning tool for the aforementioned concepts. Finally, the paper provides insights and discusses open research opportunities regarding the use of these computational tools to incorporate circular economy concepts in higher education degrees. Our findings show that, while efforts are being made to include these concepts in current programs, there is still much work to be done, especially from the point of view of university management. In addition, the analysis of the teaching methodologies analyzed shows that, although their implementation has been successful in favoring the active learning of students, their use (especially that of serious games) is not yet widespread.

In China, the power sector is currently the largest carbon emitter and the transportation sector is the fastest-growing carbon emitter. This paper proposes a model of solar-powered charging stations for electric vehicles to mitigate problems encountered in China’s renewable energy utilization processes and to cope with the increasing power demand by electric vehicles for the near future. This study applies the proposed model to Shenzhen City to verify its technical and economic feasibility. Modeling results showed that the total net present value of a photovoltaic power charging station that meets the daily electricity demand of 4500 kWh is $3,579,236 and that the cost of energy of the combined energy system is $0.098/kWh. In addition, the photovoltaic powered electric vehicle model has pollutant reduction potentials of 99.8%, 99.7% and 100% for carbon dioxide, sulfur dioxide, and nitrogen oxides, respectively, compared with a traditional gasoline-fueled car. Sensitivity analysis results indicated that interest rate has a relatively strong influence on COE (Cost of Energy). An increase in the interest rate from 0% to 6% increases COE from $0.027/kWh to $0.097/kWh. This analysis also suggests that carbon pricing promotes renewable energy only when the price of carbon is above $20/t.

Hydrogen sulfide (H2S) is a corrosive trace gas present in biogas produced from anaerobic digestion systems that should be removed to reduce engine-generator set maintenance costs. This study was conducted to provide a more complete understanding of two H2S scrubbers in terms of efficiency, operational and maintenance parameters, capital and operational costs, and the effect of scrubber management on sustained H2S reduction potential. For this work, biogas H2S, CO2, O2, and CH4 concentrations were quantified for two existing H2S scrubbing systems (iron-oxide scrubber, and biological oxidation using air injection) located on two rural dairy farms. In the micro-aerated digester, the variability in biogas H2S concentration (average: 1938 ± 65 ppm) correlated with the O2 concentration (average: 0.030 ± 0.004%). For the iron-oxide scrubber, there was no significant difference in the H2S concentrations in the pre-scrubbed (450 ± 42 ppm) and post-scrubbed (430 ± 41 ppm) biogas due to the use of scrap iron and steel wool instead of proprietary iron oxide-based adsorbents often used for biogas desulfurization. Even though the capital and operating costs for the two scrubbing systems were low (<$1500/year), the lack of dedicated operators led to inefficient performance for the two scrubbing systems.

Various problems often arise in high-rise buildings during the winter months due to the stack effect. In this study, the high-rise building of interest, located in South Korea, was experiencing constant loud noises in the winter due to the stack effect. Thus, we created a noise level reduction plan by creating a method for pressurizing the high-rise zones of the building according to outdoor conditions. To discover the appropriate pressurization operating modes, we applied a two-year commissioning process to the 50-story building of interest. The 1st- and 47th-floor elevator halls were identified to have the highest noise levels of all other floors. Prior to applying the reduction plan, the maximum noise level on the first floor with the HVAC system turned off was 85 dB(A) and with the HVAC system turned on it was 70 dB(A). Both values exceeded the criteria of 57 dB(A) for a lobby space of a commercial building. In the case of the 47th floor, the maximum noise level with the HVAC system turned off was 58.7 dB(A) and with the HVAC system turned off was 56.0 dB(A), despite the latter having increased airtightness performance and applying preliminary pressurization (i.e., HVAC operation mode 2). These values exceeded the criteria of 48 dB(A) for an elevator hall in a commercial building. Following this initial data, we determined to pressurize the high/mid-rise zones of the building according to the outdoor air temperature and wind velocity conditions, which we categorized into four types (i.e., HVAC operation mode 4). To this effect, the first-floor elevator hall’s maximum noise level was 56.6 dB(A), meeting the criteria, and the 47th-floor elevator hall’s maximum noise level was 49.5 dB(A), still exceeding the criteria but by an insignificant amount. Although the HVAC pressurization operation we utilized resulted in favorable results for the target building A, it may not be as effective in other new high-rise buildings, creating changes to the indoor air environment or to the energy costs in maintaining a building. However, for the purposes of resolving the stack effect, we believe that the commissioning process we took to optimize the HVAC operation that is presented here can be applied to other new and existing high-rise commercial buildings.

Liquid biowaste represents more than 98% of the total municipal waste streams on wet basis and 4–5% on dry basis. Recent attention has been focused on how to manage it optimally, and several novel technologies are being developed to valorize it. Among the developing alternatives is a technology that operates continuously by integrating a hydrothermal reactor, a gasifier and condenser to recover hydrochar using any produced gases to power the system. This study introduces the “3-step evolution model” in order to simulate the hydrothermal reactor. The model has been developed in a MATLAB/Cantera environment and calculates the outputs as the products of a series of sub-stoichiometric char-gas reactions. Experiments with chicken manure slurry as feedstock were implemented for the validation of the model. Treatment of 32.16 kg/h of chicken manure produces 4.57 kg/h of hydrochar and 3.45 kg/h of syngas. The 3-step evolution model simulated the correct ratio of solid-to-gas, 57–43% (excluding the liquids). The experimentally measured carbon dioxide is used as a correction factor to calculate all the other parameters that cannot be assessed during the continuous operation of the hydrothermal reactor. The simulated compositions for carbon dioxide and methane were 94–96% and 0.5–0.8%, respectively. The values were close to the experimental results that ranged from 94.7% to 95.6% for the carbon dioxide and from 0.5% to 0.7% for the methane. The model predicts that higher temperatures of operation would increase carbon monoxide composition from 4–5% up to 7–8%.

In this paper, petroleum product (mainly petrol and diesel) consumption in the transportation sector of China is analyzed. This was based on the Bayesian linear regression theory and Markov Chain Monte Carlo method (MCMC), establishing a demand-forecast model of petrol and diesel consumption introduced into the analytical framework with explanatory variables of urbanization level, per capita GDP, turnover of passengers (freight) in aggregate (TPA, TFA), and civilian vehicle number (CVN) and explained variables of petrol and diesel consumption. Furthermore, we forecast the future consumer demand for oil products during “The 12th Five Year Plan” (2011–2015) based on the historical data covering from 1985 to 2009, finding that urbanization is the most sensitive factor, with a strong marginal effect on petrol and diesel consumption in this sector. From the viewpoint of prediction interval value, urbanization expresses the lower limit of the predicted results, and CVN the upper limit of the predicted results. Predicted value from other independent variables is in the range of predicted values which display a validation range and reference standard being much more credible for policy makers. Finally, a comparison between the predicted results from autoregressive integrated moving average models (ARIMA) and others is made to assess our task.

Biomass and renewable resources are becoming substitutes for fossil-based resources, providing opportunities for more sustainable environmental management and reductions in environmental damage. This paper studies the prospects for wood pellet production in Kazakhstan through the lens of business model adjustment in a microenterprise in Kazakhstan. This study focuses on answering the following questions: (1) How do microenterprises propose, create, deliver and capture value through business models in the wood industry? (2) What are the opportunities and challenges relating to these business models in the context of wood pellet production in Kazakhstan? Kazakhstan has a high potential for biomass production, providing a particularly interesting case for analysing how microenterprises can tap into this potential to create value. This paper combines an analysis of bioenergy and forestry trends with a qualitative case study. The analysis of the business model is based on Osterwalder’s business model canvas. The value proposition of the enterprise studied herein is to provide a local biomass-based alternative to fossil fuels. The overall growth of wood-based industries in Kazakhstan and the national movement towards renewable energy create favourable prospects for microenterprises engaged in the production of wood pellets; however, these industries are also characterised by high institutional and regulatory dependencies.