Showing 1 - 2 of 2 Items

Voluntary sustainability standards could significantly reduce detrimental impacts of global agriculture

Date: 2019-02-05

Creator: W. K. Smith, E. Nelson, J. A. Johnson, S. Polasky, J. C., Milder, J. S. Gerber, P. C. West, S. Siebert, K. A. Brauman

Access: Open access

Voluntary sustainability standards (VSS) are stakeholder-derived principles with measurable and enforceable criteria to promote sustainable production outcomes. While institutional commitments to use VSS to meet sustainable procurement policies have grown rapidly over the past decade, we still have relatively little understanding of the (i) direct environmental benefits of large-scale VSS adoption; (ii) potential perverse indirect impacts of adoption; and (iii) implementation pathways. Here, we illustrate and address these knowledge gaps using an ecosystem service modeling and scenario analysis of Bonsucro, the leading VSS for sugarcane. We find that global compliance with the Bonsucro environmental standards would reduce current sugarcane production area (−24%), net tonnage (−11%), irrigation water use (−65%), nutrient loading (−34%), and greenhouse gas emissions from cultivation (−51%). Under a scenario of doubled global sugarcane production, Bonsucro adoption would further limit water use and greenhouse gas emissions by preventing sugarcane expansion into water-stressed and high-carbon stock ecosystems. This outcome was achieved via expansion largely on existing agricultural lands. However, displacement of other crops could drive detrimental impacts from indirect land use. We find that over half of the potential direct environmental benefits of Bonsucro standards under the doubling scenario could be achieved by targeting adoption in just 10% of global sugarcane production areas. However, designing policy that generates the most environmentally beneficial Bonsucro adoption pathway requires a better understanding of the economic and social costs of VSS adoption. Finally, we suggest research directions to advance sustainable consumption and production.

Using landscape metrics to characterize towns along an urban-rural gradient

Date: 2021-10-01

Creator: Abigail Kaminski, Dana Marie Bauer, Kathleen P. Bell, Cynthia S. Loftin, Erik J., Nelson

Access: Open access

Context: Urban-rural gradients are useful tools when examining the influence of human disturbances on ecological, social and coupled systems, yet the most commonly used gradient definitions are based on single broad measures such as housing density or percent forest cover that fail to capture landscape patterns important for conservation. Objectives: We present an approach to defining urban–rural gradients that integrates multiple landscape pattern metrics related to ecosystem processes important for natural resources and wildlife sustainability. Methods: We develop a set of land cover composition and configuration metrics and then use them as inputs to a cluster analysis process that, in addition to grouping towns with similar attributes, identifies exemplar towns for each group. We compare the outcome of the cluster-based urban-rural gradient typology to outcomes for four commonly-used rule-based typologies and discuss implications for resource management and conservation. Results: The resulting cluster-based typology defines five town types (urban, suburban, exurban, rural, and agricultural) and notably identifies a bifurcation along the gradient distinguishing among rural forested and agricultural towns. Landscape patterns (e.g., core and islet forests) influence where individual towns fall along the gradient. Designations of town type differ substantially among the five different typologies, particularly along the middle of the gradient. Conclusions: Understanding where a town occurs along the urban-rural gradient could aid local decision-makers in prioritizing and balancing between development and conservation scenarios. Variations in outcomes among the different urban-rural gradient typologies raise concerns that broad-measure classifications do not adequately account for important landscape patterns. We suggest future urban-rural gradient studies utilize more robust classification approaches.