The output of construction and demolition (C&D) waste in China has been rapidly increasing in the past decades. The direct landfill of such construction and demolition waste without any treatment accounts for about 98%. Therefore, recycling and utilizing this waste is necessary. The prediction of the output of such waste is the basis for waste disposal and resource utilization. This study takes Shandong Province as a case, the current output of C&D waste is analyzed by building area estimation method, and the output of C&D waste in the next few years is also predicted by Mann–Kendall trend test and quadratic exponential smoothing prediction method. Results indicate that the annual productions of C&D waste in Shandong Province demonstrates a significant growth trend with average annual growth of 11.38%. The growth rates of each city differ a lot. The better the city’s economic development, the higher the level of urbanization, the more C&D waste generated. The prediction results suggest that the output of C&D waste in Shandong Province will grow at an average rate of 3.07% in the next few years. By 2025, the amount of C&D waste will reach 141 million tons. These findings can provide basic data support and reference for the management and utilization of C&D waste.

Economic cost is decisive for the development of different power generation. Life cycle cost (LCC) is a useful tool in calculating the cost at all life stages of electricity generation. This study improves the levelized cost of electricity (LCOE) model as the LCC calculation methods from three aspects, including considering the quantification of external cost, expanding the compositions of internal cost, and discounting power generation. The improved LCOE model is applied to three representative kinds of power generation, namely, coal-fired, biomass, and wind power in China, in the base year 2015. The external cost is quantified based on the ReCiPe model and an economic value conversion factor system. Results show that the internal cost of coal-fired, biomass, and wind power are 0.049, 0.098, and 0.081 USD/kWh, separately. With the quantification of external cost, the LCCs of the three are 0.275, 0.249, and 0.081 USD/kWh, respectively. Sensitivity analysis is conducted on the discount rate and five cost factors, namely, the capital cost, raw material cost, operational and maintenance cost (O&M cost), other annual costs, and external costs. The results provide a quantitative reference for decision makings of electricity production and consumption.

Municipal solid waste (MSW) is posing great challenge for most countries in the world, which can cause severe negative impacts to the environment and human health. Waste-to-energy has great potential in China because of its technological maturity and policy support at the national level. However, there are significant conflicts between the huge market demand and strong public opposition. It is imperative to examine the public perception of waste-to-energy, especially for developing countries where a large number of projects are under construction or have been approved. The public perception of waste-to-energy was carried out by a questionnaire survey in this research. A total of 650 questionnaires were distributed and 629 questionnaires were returned, with a response rate of 96.8%. The results show that the public showed general concern in regard to environmental issues. Respondents had an overall positive attitude towards waste-to-energy, but it varied according to the demographic details of residents, such as age, education, and income. Recognition level of the benefits was higher than the concern of associated risks. Multiple linear regression shows that awareness of environmental issues had no impact on public attitude towards waste-to-energy, while public awareness and perceived benefits had notable positive impacts. Perceived risks had a positive correlation with public attitude. In order to promote the development of MSW incinerators, the government should make more publicity efforts. Rural residents, people over 50 years old, and people with low education and low income are the major groups which should be focused on to enhance the public perception. The findings provide a theoretical and practical reference for enhancing the social acceptance of waste-to-energy development.

Two methods of natural ecosystem assessment—emergy analysis (EMA) and life cycle assessment (LCA)—are reviewed in this paper. Their advantages, disadvantages, and application areas are summarized, and the similarities and differences between these two evaluation methods are analyzed respectively. Their research progress is also sorted out. The study finds that EMA and LCA share common attributes in evaluation processes and research fields, but they focus on different aspects of macrocosms and microcosms. The assessment of system sustainability is valued by both EMA and LCA, but the former has unique advantages in natural system input analysis, and the latter is more convincing in assessing environmental loading capacity. If the system boundaries of the two methods are expanded, in other words, factors such as ecosystem services, labor, and infrastructure construction are integrated into the upstream of the target system, and environmental impact is further analyzed using LCA in the downstream of the system, the two approaches would complete each other. The quantified results would be more objective. Therefore, these two theories have the necessity of coupling development. After reviewing recent coupling application cases, the results show that LCA and EMA have commonality in the upstream of the target system (mainly in inventory database construction), while the environmental impact assessment methods are different in the downstream. So the overall coupling analysis method is not formed. The current paper gives rational suggestions on the coupling development of the two systems in terms of the aggregate emergy flow table, the indicator system construction and indicator evaluation methods. In addition, it is necessary to introduce sensitivity analysis and uncertainty analysis in order to improve the reliability of assessment results. At present, the research on the coupling development of the two theories is in rapid development stage, but there are still many problems that need further exploration.