An official website of the United States government
A complex network framework for the efficiency and resilience trade-off in global food trade
- Award ID(s):
- 1844773
- PAR ID:
- 10307305
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Environmental Research Letters
- Volume:
- 16
- Issue:
- 10
- ISSN:
- 1748-9326
- Page Range / eLocation ID:
- Article No. 105003
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
Graphs are widely found in social network analysis and e-commerce, where Graph Neural Networks (GNNs) are the state-of the-art model. GNNs can be biased due to sensitive attributes and network topology. With existing work that learns a fair node representation or adjacency matrix, achieving a strong guarantee of group fairness while preserving prediction accuracy is still challenging, with the fairness-accuracy trade-off remaining obscure to human decision-makers. We first define and analyze a novel upper bound of group fairness to optimize the adjacency matrix for fairness without significantly h arming prediction accuracy. To understand the nuance of fairness-accuracy tradeoff, we further propose macroscopic and microscopic explanation methods to reveal the trade-offs and the space that one can exploit. The macroscopic explanation method is based on stratified sampling and linear programming to deterministically explain the dynamics of the group fairness and prediction accuracy. Driving down to the microscopic level, we propose a path-based explanation that reveals how network topology leads to the tradeoff. On seven graph datasets, we demonstrate the novel upper bound can achieve more efficient fairness-accuracy trade-offs and the intuitiveness of the explanation methods can clearly pinpoint where the trade-off is improved.more » « less
-
We study a flexible class of trade models with international production networks and arbitrary wedge‐like distortions like markups, tariffs, or nominal rigidities. We characterize the general equilibrium response of variables to shocks in terms of microeconomic statistics. Our results are useful for decomposing the sources of real GDP and welfare growth, and for computing counterfactuals. Using the same set of microeconomic sufficient statistics, we also characterize societal losses from increases in tariffs and iceberg trade costs and dissect the qualitative and quantitative importance of accounting for disaggregated details. Our results, which can be used to compute approximate and exact counterfactuals, provide an analytical toolbox for studying large‐scale trade models and help to bridge the gap between computation and theory.more » « less
-
null (Ed.)Global agriculture consumes substantial resources and produces significant pollution. By shifting its production to new locations, and inducing changes in technology and input use, trade has a substantial impact on environmental sustainability of the world's food systems, but due to suboptimal environmental policy, the exact nature of these impacts is in dispute. We review the literature on agricultural trade and environmental sustainability, highlighting the different approaches taken in ecology versus economics. While useful in identifying environmental costs, much of the ecological literature does not compare these costs to a trade-free counterfactual and can therefore be misleading. Further, by moving production to places with more resources and increasing production efficiency, trade can reduce the environmental impact of food production. On the other hand, trade can also limit the effectiveness of domestic environmental policy because production can be shifted to countries with less stringent regulations. However, recently, consumers are leveraging trade policy to induce exporters to improve environmental sustainability. While such policies are gaining traction in wealthy countries, evidence suggests that such measures will not reach their potential without buy-in from decision makers in the countries where the environmental damages are occurring. Expected final online publication date for the Annual Review of Resource Economics, Volume 13 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.more » « less
