To prevent deforestation and benefit locals, we propose an area approach: Designate an area containing forest and locals who can coordinate and transfer utility. Provide locals positive incentives conditional on no deforestation in that area. If entrants would violate that condition, provide locals a positive incentive instead for "area regeneration"- if deforestation occurs on land in the area, the forest regenerates on that land. We model the resulting strategic interactions by a cooperative game in partition correspondence form, wherein locals can observe any deforestation and "block" the perpetrator from establishing a farm or other profitable land-use, allowing the forest to regenerate. Depending on the cost to do that blocking, number of potential entrants, and each local's value of engaging in deforestation, we characterize the feasible incentives to prevent deforestation in the area, and to do so with compensation (making every local as well off as with deforestation). By surveying smallholder palm farmers in 58 villages of East Kalimantan, Indonesia, we fit our model to evaluate our area approach, with the area a village and the incentive a price premium for palm fruit. A price premium is an imperfect incentive, having least value for a farmer with the least land and most temptation to engage in deforestation. Nevertheless, though the existing Roundtable on Sustainable Palm Oil (RSPO) price premium is too low, with a moderate price premium, our area approach prevents deforestation in most villages. The area regeneration approach is remarkably robust to deter potential entrants.

Increasing the welfare of smallholder farmers in developing countries plays a crucial role in the global effort to reduce worldwide poverty and hunger. On the one hand, smallholders represent a large proportion of the world’s poor and, on the other, they produce the majority of the food consumed in developing countries. This realization has led governments and organizations around the world to implement policies aimed at increasing farmers’ yields. Although most of these policies have resulted in welfare increases, the environmental effects have been varied. While in many settings intensification policies have been linked to a decrease in deforestation, in many other settings the reverse is true. In this chapter we propose a novel explanation of these seemingly contradictory results. We achieve this through studying a detailed operational model of a farmer’s dynamic decisions of land-clearing and production. We show the importance of considering the interaction between random production costs and liquidity constraints faced by smallholder farmers. These two elements are key to our main result: a reduction in the cost of intensification can lead to lower deforestation rates when the variation in production costs is high enough compared to the cost of intensification. Alternatively, the same reduction in the cost of intensification may lead to higher deforestation rates if the variation in production costs is low enough compared to the cost of intensification. This result helps explain the discrepancies seen in practices and may allow policy makers to better target interventions in order to achieve win-win situations: improvement of smallholder welfare and protection of the natural forest.

To respond to pandemics such as COVID-19, policy makers have relied on interventions that target specific population groups or activities. Because targeting is operationally challenging and contentious, rigorously quantifying its benefits and designing practically implementable policies that achieve some of these benefits is critical for effective and equitable pandemic control. We propose a flexible framework that leverages publicly available data and a novel optimization algorithm based on model predictive control and trust region methods to compute optimized interventions that can target two dimensions of heterogeneity: age groups and the specific activities that individuals normally engage in. We showcase a complete implementation focused on the Ile-de-France region of France and use this case study to quantify the benefits of dual targeting and to propose practically implementable policies. We find that dual targeting can lead to Pareto improvements, reducing the number of deaths and the economic losses. Additionally, dual targeting allows maintaining higher activity levels for most age groups and, importantly, for those groups that are most confined, thus leading to confinements that are arguably more equitable. We then fit decision trees to explain the decisions and gains of dual-targeted policies and find that they prioritize confinements intuitively, by allowing increased activity levels for group-activity pairs with high marginal economic value prorated by social contacts, which generates important complementarities. Because dual targeting can face significant implementation challenges, we introduce two practical proposals inspired by real-world interventions — based on curfews and recommendations — that achieve a significant portion of the benefits without explicitly discriminating based on age.

Many operational settings share the following three features: (i) a centralized planning system allocates tasks to workers or service providers, (ii) the providers generate value by completing the tasks, and (iii) the completion of tasks influences the providers’ welfare. In such cases, the planning system’s allocations often entail trade-offs between the service providers’ welfare and the total value that is generated (or that accrues to the system itself), and concern arises that allocations that are good under one metric may perform poorly under the other. We propose a broad framework for quantifying the magnitude of value losses when allocations are restricted to satisfy certain desirable guarantees to the service providers. We consider a general class of guarantees that includes many considerations of practical interest arising (e.g., in the design of sustainable two-sided markets) in workforce welfare and compensation, or in sourcing and payments in supply chains, among other application domains. We derive tight bounds on the relative value loss and show that this loss is limited for any restriction included in our general class. Our analysis shows that when many providers are present, the largest losses are driven by fairness considerations, whereas when few providers are present, they are driven by the heterogeneity in the providers’ effectiveness to generate value; when providers are perfectly homogenous, the losses never exceed 50%. We study additional loss drivers and find that less variability in the value of jobs and a more balanced supply-demand ratio may lead to larger losses. Lastly, we demonstrate numerically using both real-world and synthetic data that the loss can be small in several cases of practical interest.

Given a simple graph G, a set 𝐶⊆𝑉(𝐺) is a neighborhood cover set if every edge and vertex of G belongs to some G[v] with 𝑣∈𝐶, where G[v] denotes the subgraph of G induced by the closed neighborhood of the vertex v. Two elements of 𝐸(𝐺)∪𝑉(𝐺) are neighborhood-independent if there is no vertex 𝑣∈𝑉(𝐺) such that both elements are in G[v]. A set 𝑆⊆𝑉(𝐺)∪𝐸(𝐺) is neighborhood-independent if every pair of elements of S is neighborhood-independent. Let 𝜌_n(𝐺) be the size of a minimum neighborhood cover set and 𝛼_n(𝐺) of a maximum neighborhood-independent set. Lehel and Tuza defined neighborhood-perfect graphs G as those where the equality 𝜌_n(𝐺′)=𝛼_n(𝐺′) holds for every induced subgraph 𝐺′ of G. In this work we prove forbidden induced subgraph characterizations of the class of neighborhood-perfect graphs, restricted to two superclasses of cographs: 𝑃₄-tidy graphs and tree-cographs. We give as well linear-time algorithms for solving the recognition problem of neighborhood-perfect graphs and the problem of finding a minimum neighborhood cover set and a maximum neighborhood-independent set in these same classes. Finally we prove that although for complements of trees finding these optimal sets can be achieved in linear-time, for complements of bipartite graphs it is NP-hard.

Environmental science documents that agricultural production is a dominant driver of deforestation in developing countries. Complex land tenure systems coupled with costly enforcement of conservation laws enable farmers to convert tropical forests into productive land in search of a better income. In this paper, we model the incentives and specific decision-making processes of farmers who dynamically choose their land expansion in response to specific economic and value-chain circumstances. We analyze several interventions that balance forest protection and farmers’ welfare.