Traditional integrated modeling (IM) is based on developing and aggregating all relevant (sub)models and data into a single integrated linear programming (LP) model. Unfortunately, this approach is not applicable for IM under asymmetric information (ASI), i.e., when “private” information regarding sectoral/regional models is not available, or it cannot be shared by modeling teams (sectoral agencies). The lack of common information about LP submodels makes LP methods inapplicable for integrated LP modeling. The aim of this paper is to develop a new approach to link and optimize distributed sectoral/regional optimization models, providing a means of decentralized cross-sectoral coordination in the situation of ASI. Thus, the linkage methodology enables the investigation of policies in interdependent systems in a “decentralized” fashion. For linkage, the sectoral/regional models do not need recoding or reprogramming. They also do not require additional data harmonization tasks. Instead, they solve their LP submodels independently and in parallel by a specific iterative subgradient algorithm for nonsmooth optimization. The submodels continue to be the same separate LP models. A social planner (regulatory agency) only needs to adjust the joint resource constraints to simple subgradient changes calculated by the algorithm. The approach enables more stable and resilient systems’ performance and resource allocation as compared to the independent policies designed by separate models without accounting for interdependencies. The paper illustrates the application of the methodology to link detailed energy and agricultural production planning models under joint constraints on water and land use.

Across the world, human activity is approaching planetary boundaries. In northwest China, in particular, the coal industry and agriculture are competing for key limited inputs of land and water. In this situation, the traditional approach to planning the development of each sector independently fails to deliver sustainable solutions, as solutions made in sectorial ‘silos’ are often suboptimal for the entire economy. We propose a spatially detailed cost-minimizing model for coal and agricultural production in a region under constraints on land and water availability. We apply the model to the case study of Shanxi province, China. We show how such an integrated optimization, which takes maximum advantage of the spatial heterogeneity in resource abundance, could help resolve the conflicts around the water–food–energy (WFE) nexus and assist in its management. We quantify the production-possibility frontiers under different water-availability scenarios and demonstrate that in water-scarce regions, like Shanxi, the production capacity and corresponding production solutions are highly sensitive to water constraints. The shadow prices estimated in the model could be the basis for intelligent differentiated water pricing, not only to enable the water-resource transfer between agriculture and the coal industry, and across regions, but also to achieve cost-effective WFE management.

Countries generally agree that global greenhouse gas emissions are too high, but prefer other countries reduce emissions rather than reducing their own. The Paris Agreement is intended to solve this collective action problem, but is likely insufficient. One proposed solution is a matching-commitment agreement, through which countries can change each other’s incentives by committing to conditional emissions reductions, before countries decide on their unconditional reductions. Here, we study matching-commitment agreements between two heterogeneous countries. We find that such agreements (1) incentivize both countries to make matching commitments that in turn incentivize efficient emissions reductions, (2) reduce emissions from those expected without an agreement, and (3) increase both countries’ welfare. Matching-commitment agreements are attractive because they do not require a central enforcing authority and only require countries to fulfil their promises; countries are left to choose their conditional and unconditional emissions reductions according to their own interests.

Phosphorus (P) is an essential nutrient to boost crop yields, but P runoff can cause nutrient over-enrichment in agricultural watersheds and can lead to irreversible effects on aquatic ecosystems and their biodiversity. Lake Erie is one prominent example as this watershed has experienced multiple episodes of harmful algal blooms over the last decades. Annual P loads crucially depend on yearly weather variations, which can create the risk of years with high runoff and excessive nutrient loads. Here we apply stochastic modeling to derive sustainable management strategies that balance crop yield optimization with environmental protection, while accounting for weather variability as well as weather trends as a result of climate change. We demonstrate that ignoring annual weather variations results in mitigation efforts for environmental pollution that are largely insufficient. Accounting explicitly for future variations in precipitation allows us to control the risk of emissions exceeding the P target loads. When realistic risk targets are imposed, we find that a package of additional measures is required to avoid P over-enrichment in the Lake Erie watershed. This package consists of a substantial reduction of P inputs (approximately 30% for different accepted risk levels), adoption of cover crops throughout the near- and mid-century, and cultivation of less nutrient-intensive crops (30% more soy at the expense of corn). Although climate change reinforces these conclusions, we find that the accepted risk level of exceeding P target loads is the predominant factor in defining a sustainable nutrient management policy.