This paper describes a protocol in which female participants scored a range of sensory attributes and applied a financial value to sleeves made from knitted fabric after undertaking a 30 minute circuit training program in a gym. Each participant wore a test and a control sleeve which they scored a line scale for breathability (anchored by the words 'doesn't breathe well'/'breathes well'), skin feel (anchored by the words 'prickly itchy'/'soft smooth'), feel after exercise (anchored by the words 'damp sweaty'/'dry') and overall comfort (anchored by the words 'very uncomfortable'/'very comfortable'). Participants also estimated their willingness to pay (WTP) for two long-sleeved next-to-skin garments made from the test and the control fabrics. All sensory scores and WTP estimates were positively correlated. Participant and sleeve fit impacted on sensory scores and WTP estimates. However, the significance of participant and fit effects were reduced if differences between the test and control sleeves were analysed and therefore this difference was a better measure of the fabric attributes than simply meaning the test fabric score. The sleeve protocol provided a relatively inexpensive and simple means to score fabrics on sensory attributes and WTP.

Financial institutions are indirectly exposed to risks associated with the impacts and dependencies on natural capital and ecosystem services of the companies that they invest in, lend to, and insure. This is particularly true for banks lending to agriculture: a sector with both significant impacts and critical dependencies on natural capital. Bank lending is a vital source of new finance for the sector, which is essential to achieve sustainable intensification targets. Yet current credit decision-making practice is still based on conventional financial and management indicators, lacking any systematic assessment of natural capital risks, especially those associated with dependencies. Operationalising natural capital risk assessment requires practicable indicators and data to evaluate the most material natural capital risks for a given sub-sector and geography, but it is unclear to what extent these are available. We assess the practicability of natural capital dependency risk indicators and data sources for a critical case study of Australian sheep production. We find that at least moderately practicable indicators and data sources are available to assess the 11 major dependency risks that are material for this industry. Challenges remain in determining risk thresholds for most indicators, and quantifying risk impacts on profitability.