Bioenergy Sustainability Indicators

An indicator is defined as a summary measure that provides information on the state of, or change in, the system that is being measured. An example of an indicator is, net primary productivity per unit area and time (gC/M²-year). Indicators provide information about the potential or realized effects of human activities on environmental, social, or economic phenomena of concern. Published analyses that offer useful recommendations for bioenergy indicators are summarized in the Indicator Checklist.

Criteria for selecting indicators:

Selection and prioritization of indicators for assessing the environmental or socioeconomic effects of a specific bioenergy system depend on many factors. Fewer, more, or different indicators may be required, depending on local context (Dale et al. 2015; Efroymson et al. 2013).

To be effective, indicators must be: Practical, Sensitive and responsive to stresses; Unambiguous with respect to what is measured, how measurements are made, and how response is measured; Anticipatory of impending changes; and Sufficient when considered collectively to reflect issues prioritized by stakeholders. Interest in understanding sustainability of bioenergy systems must be balanced by support for collecting and analyzing the data that are needed to quantify it. (Dale et al. 2013).

Limitations of indicators:

Indicators can be misinterpreted unless they are transparently and consistently applied. While human endeavors will never be indefinitely sustainable, one option can be considered more sustainable than another based on a set of criteria and indicators in a defined context (e.g., ASTM 3066a 2016; Dale et al. 2015). Caution is recommended with approaches that involve sustainability indices, or measures that combine and average the values from multiple indicators as these can mask important trade-offs that are valuable for decision making (Dale et al. 2013). See the Bioenergy Sustainability Trade-offs Assessment Resource (BioSTAR) for more information.

Management statistics, such as number of producers adopting better nitrogen application procedures, or the area under a specified type of management (e.g., no-till agriculture), can be useful and are easier to measure but require validation by objective indicators to verify the degree to which intended results are being achieved by the adoption of the practice (Eichler Inwood, 2018).

Models are useful for estimating effects and making projections but to draw conclusions, simulations require calibration and validation based on observations. Objectively verifiable indicators and transparent analyses to allocate attribution among potential causal agents, are essential ingredients for science-based assessments (Efroymson et al. 2013, 2015). Thus, while indicators such as changes in carbon stocks or biodiversity are valid and important, “land-use change” that relies on simplified model constructs, may be misleading (Kline et al. 2011).