自工业革命以来，人口的快速增长，经济的飞速发展，对自然资源的掠夺式开发利用，使原有的自然生态系统遭到了极大的破坏，产生了诸如环境污染、气候变化、水土流失、生物多样性丧失等一系列危害人类自身生存的严重问题。如何在自然资源环境可承受的范围之内，实现人类和经济的可持续发展是人类面临的突出问题。为此，首先需要一种可以客观衡量和评价自然环境与人类经济关系的共同量化平台。
20世纪80年代以美国著名的生态学家、“系统生态学”之父H.T. Odum为首创立的能值理论方法，以能值为量纲实现了物质流、能量流与经济流的统一量化评价，正日益发展成为生态经济系统整合研究评价的主流方法，被誉为“环境与经济间的桥梁”。但目前，大多数的能值分析评价结果由于缺乏不确定性分析，而常常遭受到一定的质疑。虽然Ingwersen(2010)提出使用蒙特卡洛法模拟计算能值表格模型的不确定性，但需要事先确定参数的概率分布和相关性，从而限制了它的使用。
中科院华南植物园植被与景观生态学研究组的博士研究生李林军在导师任海研究员和陆宏芳研究员的指导下，借鉴国际通用的《测量不确定度评定与表示指南》，辨识了能值表格模型的两种数据类型，并分别引进了方差法和泰勒法计算能值表格模型的不确定度，然后用案例加以了验证。结果表明，当有多个系统重复样本时，方差法由于不需要任何假设且考虑了模型参数间潜在的相关性，而计算精度更可靠，且计算方法简单；当只有系统组分的重复样本数据时，泰勒法由于不需要作概率分布假设，而比蒙特卡洛法更为适用，且计算也更为方便和简单。
本研究补充完善了能值理论的不确定性评估，是将不确定性分析纳入能值分析与评价实践的重要成果，一定程度上将促进能值理论和方法的更广泛接受和应用。另外，这两种不确定性评价方法不仅适用于能值分析，理论上也能用于尺度推绎和其它生态模型的生态学研究中。
目前，该研究成果已被国际生态学研究主流期刊《生态模拟》（Ecological Modelling）登载发表。（生物谷Bioon.com）
生物谷推荐原文出处：
Ecological Modelling DOI： 10.1016/j.ecolmodel.2011.04.023
Methods for estimating the uncertainty in emergy table-form models
Linjun Li, Hongfang Lu, Daniel E. Campbell and Hai Ren
Emergy studies have suffered criticism due to the lack of uncertainty analysis and this shortcoming may have directly hindered the wider application and acceptance of this methodology. Recently, to fill this gap, the sources of uncertainty in emergy analysis were described and analytical and stochastic methods were put forward to estimate the uncertainty in unit emergy values (UEVs). However, the most common method used to determine UEVs is the emergy table-form model, and only a stochastic method (i.e., the Monte Carlo method) was provided to estimate the uncertainty of values calculated in this way. To simplify the determination of uncertainties in emergy analysis using table-form calculations, we introduced two analytical methods provided by the Guide to the Expression of Uncertainty in Measurement (GUM), i.e., the Variance method and the Taylor method, to estimate the uncertainty of emergy table-form calculations for two different types of data, and compared them with the stochastic method in two case studies. The results showed that, when replicate data are available at the system level, i.e., the same data on inputs and output are measured repeatedly in several independent systems, the Variance method is the simplest and most reliable method for determining the uncertainty of the model output, since it considers the underlying covariance of the inputs and requires no assumptions about the probability distributions of the inputs. However, when replicate data are only available at the subsystem level, i.e., repeat samples are measured on subsystems without specific correspondence between an output and a certain suite of inputs, the Taylor method will be a better option for calculating uncertainty, since it requires less information and is easier to understand and perform than the Monte Carlo method.
