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Freshwater lakes provide critical ecological services to the local ecosystem. However, many of them are facing serious challenges, such as ecosystem degradation and water contamination, due to irrational water utilization and a lack of effective management. Under such a circumstance, it is crucial to examine the ecosystem services of freshwater lakes and uncover the driving forces so that appropriate protection policies can be raised. This study aims to fill such a research gap by employing an emergy accounting method. A case study of Erhai Lake (the second largest freshwater lake in Yunnan province, southwest China) was conducted for the period of 2001–2015. Driving forces that affect ecosystem services were analyzed by using Logarithmic Mean Divisia Index (LMDI). Results show that the total ecosystem services of Erhai Lake were reduced from 334.03E + 18 sej in 2001 to 274.37E + 18 sej in 2015. This was caused by the obvious decline of regulating services and supporting services, far exceeding the increase of provisioning services and cultural services. In 2015, two types of increased services that benefit human life in the market became the primary services of Erhai Lake. And their proportions were far beyond the two reduced ones that were overlooked due to their public and free attributes. The key driving forces include economic scale factor (∆EES), the fast and intensive economic activities. This development was at the cost of environmental degradation based upon the analysis of emergy benefit factor (∆EEB). Finally, several suggestions are presented. This study provides valuable insights to understand ecosystem services of freshwater lakes so that a sustainable development pathway can be found to protect such freshwater lakes. [ABSTRACT FROM AUTHOR]

Megacities consume a large amount of energy and materials, leading to various environmental impacts. In this paper, multi-scale integrated analysis of societal and ecosystem metabolism (MuSIASEM) is applied to compare and analyze the metabolic characteristics of four global megacities. This paper extends the analytical framework of MuSIASEM to the N-3 level (detailed industries) providing a higher level of details than current applications. Based on model results, among the four global megacities, Shanghai's labor productivity and energy efficiency are lowest given its large manufacturing sector and complex industrial structure. Tokyo's labor productivity is the highest with a productive tertiary sector. London has the lowest exosomatic energy metabolic rate and a mature tertiary sector. Paris has the best social welfare system. Such results highlight key factors affecting the metabolic pathways of the respective cities and provide city planners and policy-makers critical information to support green growth and low carbon development. [ABSTRACT FROM AUTHOR]