Mitigating imported fuel dependency in agricultural production: Case study of an island nation's vulnerability to global catastrophic risks

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Abstract

A major global catastrophe would likely disrupt trade in liquid fuels. Countries dependent on imported oil products might struggle to sustain industrial agriculture. Island nations importing 100% of refined fuels are particularly vulnerable. Our case study aimed to estimate the agricultural land area and biofuel volumes needed to feed the population of New Zealand in the absence of trade. Results showed that stored diesel would quickly be exhausted with ordinary use (weeks) and even with strict rationing (months). To preserve fuel, we found that farming wheat (requiring as little as 5.4 million liters [L] of diesel per annum) was more fuel-efficient than potatoes (12.3) or dairy (38.7) to feed the national population under a climate-as-usual scenario. In a nuclear winter scenario, with reduced agricultural yields, proportionately greater diesel is needed. The wheat would require 24% of current grain-cropped land, and the canola crop used as feedstock for the required biofuel would occupy a further 1%–7%. Investment in canola biodiesel or renewable diesel refineries could ensure supply for the bare minimum agricultural liquid fuel needs. Were subsequent analysis to favor this option as part of a fuels resilience response and as a tradeoff for routine food use, expansion in refining and canola cropping before a catastrophe could be encouraged through market mechanisms, direct government investment, or a combination of these. Logistics of biofuel refining scale-up, post-catastrophe, should also be analyzed. Further, biodiesel produced in normal times would help the nation meet its emissions reduction targets. Other countries should conduct similar analyses.

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APA

Boyd, M., Ragnarsson, S., Terry, S., Payne, B., & Wilson, N. (2024). Mitigating imported fuel dependency in agricultural production: Case study of an island nation’s vulnerability to global catastrophic risks. Risk Analysis, 44(10), 2360–2376. https://doi.org/10.1111/risa.14297

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