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The object of the study is the water supply and sanitation system of an industrial enterprise. Currently, many enterprises do not use the treated industrial waste water in the water supply cycle. Therefore, the use of treated wastewater is of practical importance. The scheme of thermal treatment of waste water for obtaining pure condensate by evaporation is proposed. The main results of the study are the technical solution for the development of the enterprise's wastewater treatment complex. Pure condensate is obtained in an instant boiling evaporator. The research method is based on the calculation of energy costs in wastewater treatment for different types of waste water. The results of the study allow us to determine the optimal set of energy equipment for obtaining pure condensate and its further use. The specific practical significance lies in the development of a technical solution that helps the treatment of wastewater and the reduction of emissions into the water basin.

Abstract The presented study deals with univariate and multivariate analysis of the dependencies between the factor and consequent variables in the study of the efficiency of a heat pump unit with a radiant evaporator. The strength and direction of the connections between these variables is studied and discussed. The values of the correlation and determination coefficients were determined and the equations of the models describing them were derived. The presented analysis is considered as a valuable tool for the heating, ventilating and air conditioning design process in the building sector.

Abstract Mitigating the effects of environmental deterioration requires a focus on not just CO2 emissions from energy consumption, but also environmental pollution from industry sectors. To reach this goal, recent studies have extended ecological footprint (EF) analysis to identify the ecological drivers of various key industry sectors. The role of the phosphorus (P) industry on the EF within the environmental Kuznets curve (EKC) framework for China is the emphasis of this study. Autoregressive distributive lag (ARDL) as well as the impulse response function and robustness analysis were used to consider a time from 1985 to 2018. The study verifies the EKC hypothesis for China in both the long and the short run, and indispensable determinants are proposed to be included to assure the model’s fitness and robustness when conducting EF analysis of industry sectors. Energy consumption–based carbon emissions have been verified as the dominant contributor to EF, but P use and urbanization have a significant lagged positive influence on EF in the short run. P exports, in particular, have been highlighted as a critical driver of the EF of China’s P industry. The conducted frequency domain causality test reinforced the above findings and demonstrated bidirectional causality at different frequencies. This work suggests that formulating plausible P export policies to alleviate the conflict between the output of China’s P industry and the environmental sustainability of this industry are necessary. In this context, “multidisciplinary, multidimensional, and practical solutions” are most desirable for sustainable P management.

Abstract Since energy consumption (EC) is becoming an important issue for sustainable development in the world, it has a practical significance to predict EC effectively. However, there are two main uncertainty factors affecting the accuracy of a region's EC prediction. Firstly, with the ongoing rapid changes in society, the consumption amounts can be non-smooth or even fluctuating during a long time period, which makes it difficult to investigate the sequence's trend in order to forecast. Secondly, in a given region, it is difficult to express the consumption amount as a real number, as there are different development levels in the region, which would be more suitably described as interval numbers. Most traditional prediction models for energy consumption forecasting deal with long-term real numbers. It is seldom found to discover research that focuses specifically on uncertain EC data. To this end, a novel energy consumption forecasting model has been established by expressing ECs in a region as interval grey numbers combining with the optimized discrete grey model (DGM(1,1)) in Grey System Theory (GST). To prove the effectiveness of the method, per capita annual electricity consumption in southern Jiangsu of China is selected as an example. The results show that the proposed model reveals the best accuracy for the short data sequences (the average fitting error is only 2.19% and the average three-step forecasting error is less than 4%) compared with three GM models and four classical statistical models. By extension, any fields of EC, such as petroleum consumption, natural gas consumption, can also be predicted using this novel model. As the sustained growth in EC of China's, it is of great significance to predict EC accurately to manage serious energy security and environmental pollution problems, as well as formulating relevant energy policies by the government.

In the past 10 years, the implementation of grassland ecological compensation policy has played an important role in the sustainable development of the pastoral economy. How much impact on herders will delineating the ecological conservation redline have? Such delineation is significant for the smooth implementation of the ecological conservation redline. Based on this, taking the three banner counties with a large area under the control area of the ecological redline of the Xilin Gol as examples, OLS and quantile regression were used to analyze the impact of a grassland ecological compensation policy on herdsmen’s income level, and the ordered Probit model was used to analyze the influencing factors of herdsmen’s satisfaction with the policy. The results show that: (1) grassland ecological compensation has a significant positive impact on low-income herders in ecological protection redline areas; (2) grassland ecological compensation, income and the implementation of current policy have a positive impact on the satisfaction of herders in the redline area; (3) herdsmen are highly satisfied with the grassland ecological compensation policy, but there is still a lot of room for improvement in the compensation policy after the redline is delineated. In this regard, we should increase compensation for areas with a high proportion of ecological conservation redlines, and explore ways to increase income from animal husbandry products. At the same time, we should strengthen the publicity of ecological protection redline policies and promote the timely disbursement of funds, reconstruct the grassland ecological compensation mechanism by strengthening policy incentives and hardening regulatory constraints, and effectively improve the policy efficiency of ecological protection redlines.

In this study, Distribution of Relaxation Times (DRT) was successfully demonstrated in the analysis of the impedance spectra of High-Temperature Polymer Electrolyte Membrane Fuel Cells (HT-PEMFC) doped with phosphoric acid. Electrochemical impedance spectroscopy (EIS) was performed and the quality of the recorded spectra was verified by Kramers-Kronig relations. DRT was then applied to the measured spectra and polarization losses were separated on the basis of their typical time constants. The main features of the distribution function were assigned to the cell’s polarization processes by selecting appropriate experimental conditions. DRT can be used to identify individual internal HT-PEMFC fuel cell phenomena without any a-priori knowledge about the physics of the system. This method has the potential to further improve EIS spectra interpretation with either equivalent circuits or physical models.

Agro-wastes, such as crop residues, leaf litter, and sawdust, are major contributors to global greenhouse gas emissions, and consequently a major concern for climate change. Nowadays, mushroom cultivation has appeared as an emerging agribusiness that helps in the sustainable management of agro-wastes. However, partial utilization of agro-wastes by mushrooms results in the generation of a significant quantity of spent mushroom substrates (SMS) that have continued to become an environmental problem. In particular, Shiitake (Lentinula edodes Berk.) mushrooms can be grown on different types of agro-wastes and also generate a considerable amount of SMS. Therefore, this study investigates the biotransformation of SMS obtained after Shiitake mushroom cultivation into biogas and attendant utilization of slurry digestate (SD) in tomato (Solanum lycopersicum L.) crop fertilization. Biogas production experiments were conducted anaerobically using four treatments of SMS, i.e., 0% (control), 25, 50, and 75% inoculated with a proportional amount of cow dung (CD) as inoculum. The results on biogas production revealed that SMS 50% treatment yielded the highest biogas volume (8834 mL or 11.93 mL/g of organic carbon) and methane contents (61%) along with maximum reduction of physicochemical and proximate parameters of slurry. Furthermore, the biogas digestate from 50% treatment further helped to increase the seed germination (93.25%), seedling length (9.2 cm), seedling root length (4.19 cm), plant height (53.10 cm), chlorophyll content (3.38 mg/g), total yield (1.86 kg/plant), flavonoids (5.06 mg/g), phenolics (2.78 mg/g), and tannin (3.40 mg/g) contents of tomato significantly (p < 0.05) in the 10% loading rate. The findings of this study suggest sustainable upcycling of SMS inspired by a circular economy approach through synergistic production of bioenergy and secondary fruit crops, which could potentially contribute to minimize the carbon footprints of the mushroom production sector.

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.