基于生态系统服务视角的人类福祉评估技术方法体系

不管是联合国2030可持续发展目标，还是我国的生态文明建设，最终的目标都是协调好人与自然的关系，保证生态系统服务的可持续供给以提高人类福祉。过去二十年来，随着生态系统服务成为国际生态学研究的前沿和热点，生态系统服务与人类福祉之间的关系也得到越来越多的重视。而定量刻画二者之间关系的前提是在一个统一的概念框架下建立各自的量化评估指标体系。不过，目前大部分的研究集中在生态系统服务的评估，而对人类福祉评估的研究相对匮乏，因此急需一套对人类福祉进行评估的技术方法。笔者结合多年的理论和实践，对人类福祉评估的理论基础与技术方法进行了系统地归纳与整理，以期为生态系统服务与人类福祉之间量化关系的研究提供参考。

Methodologies of human well-being assessment from the ecosystem service perspective

No matter the United Nation 2030 Sustainable Development Goals or China''s ecological civilization construction, the ultimate goal is to coordinate the relationships between human and nature, so as to sustain the provision of ecosystem goods and services to improve human well-being. During the past two decades, as ecosystem service research becomes the frontier and hotspot, there has been more and more attention paid to the linkages between ecosystem services and human well-being. To quantify their linkages, the premise is to establish corresponding index systems to quantify ecosystem services and human well-being, respectively. Nevertheless, the majority of current research focuses on ecosystem service assessment, there are relatively less studies on human well-being assessment that is compatible to the ecosystem service framework. Therefore, it is urgently needed to provide a set of methodologies for developing human well-being indices taking the linkages between ecosystem services and human well-being into consideration. Based on many years'' theoretic analyses and practices, we synthesized the theoretic bases and methodologies for human well-being assessment. We compared the advantages and disadvantages of different methods. We recommend the improved factor analysis approach for five main reasons. First, the improved factor analysis approach provides a general and pragmatic composite index development method that combines the theoretic design with data mining technique, which largely improves the internal and external consistency of developed human well-being index system. Second, the improved factor analysis approach is scale free (e.g., from household, township, to county and national levels) and can also be applied to different conceptual framework. This is particularly useful to design studies under different ecological and socioeconomic contexts. Thirdly, the improved factor analysis approach is flexible for weighting setting. The weighting can be generated by data variation or be customized by management goals. Fourthly, the improved factor analysis approach does not have the measurement error free assumption and is more robust, especially for secondary data. Considering it is often time consuming and costly to collect first hand data, such advantage makes it is possible to develop effective human well-being indices based on secondary data (e.g., statistical yearbook data). Finally, the improved factor analysis approach produces a composite index system, which includes the overall index and sub-indices. Such index system can be used both for analyzing large-scale spatiotemporal dynamics and small-scale detailed linkages between different ecosystem services and various dimensions of human well-being. We hope that our synthesis will lay a foundation for the quantification of linkages between ecosystem services and human well-being.