arXiv subjects

Kevin Bradley Dsouza

Publications and source records attributed to Kevin Bradley Dsouza.

7 recordsLinked to original sources

arXiv2025

Structuring Collective Action with LLM-Guided Evolution: From Ill-Structured Problems to Executable Heuristics

Collective action problems, which require aligning individual incentives with collective goals, are classic examples of Ill-Structured Problems (ISPs). For an individual agent, the causal links between local actions and global outcomes are unclear, stakeholder objectives often conflict, and no single, clear algorithm can bridge micro-level choices with macro-level welfare. We present ECHO-MIMIC, a general computational framework that converts this global complexity into a tractable, Well-Structured Problem (WSP) for each agent by discovering executable heuristics and persuasive rationales. The framework operates in two stages: ECHO (Evolutionary Crafting of Heuristics from Outcomes) evolves snippets of Python code that encode candidate behavioral policies, while MIMIC (Mechanism Inference \& Messaging for Individual-to-Collective Alignment) evolves companion natural language messages that motivate agents to adopt those policies. Both phases employ a large-language-model-driven evolutionary search: the LLM proposes diverse and context-aware code or text variants, while population-level selection retains those that maximize collective performance in a simulated environment. We demonstrate this framework on two distinct ISPs: a canonical agricultural landscape management problem and a carbon-aware EV charging time slot usage problem. Results show that ECHO-MIMIC discovers high-performing heuristics compared to baselines and crafts tailored messages that successfully align simulated agent behavior with system-level goals. By coupling algorithmic rule discovery with tailored communication, ECHO-MIMIC transforms the cognitive burden of collective action into a implementable set of agent-level instructions, making previously ill-structured problems solvable in practice and opening a new path toward scalable, adaptive policy design.

cs.MA↗

arXiv2025

BoreaRL: A Multi-Objective Reinforcement Learning Environment for Climate-Adaptive Boreal Forest Management

Boreal forests store 30-40\% of terrestrial carbon, much in climate-vulnerable permafrost soils, making their management critical for climate mitigation. However, optimizing forest management for both carbon sequestration and permafrost preservation presents complex trade-offs that current tools cannot adequately address. We introduce BoreaRL, the first multi-objective reinforcement learning environment for climate-adaptive boreal forest management, featuring a physically-grounded simulator of coupled energy, carbon, and water fluxes. BoreaRL supports two training paradigms: site-specific mode for controlled studies and generalist mode for learning robust policies under environmental stochasticity. Through evaluation of multi-objective RL algorithms, we reveal a fundamental asymmetry in learning difficulty: carbon objectives are significantly easier to optimize than thaw (permafrost preservation) objectives, with thaw-focused policies showing minimal learning progress across both paradigms. In generalist settings, standard gradient-descent based preference-conditioned approaches fail, while a naive site selection approach achieves superior performance by strategically selecting training episodes. Analysis of learned strategies reveals distinct management philosophies, where carbon-focused policies favor aggressive high-density coniferous stands, while effective multi-objective policies balance species composition and density to protect permafrost while maintaining carbon gains. Our results demonstrate that robust climate-adaptive forest management remains challenging for current MORL methods, establishing BoreaRL as a valuable benchmark for developing more effective approaches. We open-source BoreaRL to accelerate research in multi-objective RL for climate applications.

cs.LG↗

arXiv2025

Boreal Afforestation's Underestimated Cloud Influence on Earth's Energy Imbalance

Earth's Energy Imbalance (EEI) is accelerating, partly due to declining planetary albedo from reduced cloud cover. Boreal afforestation can either mitigate or exacerbate this trend through competing biophysical feedbacks. While snow masking reduces surface albedo (+0.5 to +2.5 $W/m^2$ warming), forests can enhance low-level cloud cover (0.1-0.5%) and increase cloud reflectivity via biogenic volatile organic compounds (BVOCs), producing potential cooling (-1.8 to -6.7 $W/m^2$). This BVOC-aerosol-cloud pathway remains poorly constrained but may dominate under warmer conditions, challenging carbon-centric mitigation paradigms. Large-scale initiatives (e.g., Canada's 2 Billion Tree Commitment) risk unintended warming if not climate-smartly sited. We present a tiered decision-support framework that integrates biogeochemical and biophysical processes, explicitly incorporating cloud-aerosol feedbacks. Urgent inclusion of these feedbacks in policy is essential to ensure boreal afforestation contributes to EEI stabilization.

physics.ao-ph↗

arXiv2025

Bridging Farm Economics and Landscape Ecology for Global Sustainability through Hierarchical and Bayesian Optimization

Agricultural landscapes face the dual challenge of sustaining food production while reversing biodiversity loss. Agri-environmental policies often fall short of delivering ecological functions such as landscape connectivity, in part due to a persistent disconnect between farm-level economic decisions and landscape-scale spatial planning. We introduce a novel hierarchical optimization framework that bridges this gap. First, an Ecological Intensification (EI) model determines the economically optimal allocation of land to margin and habitat interventions at the individual farm level. These farm-specific intervention levels are then passed to an Ecological Connectivity (EC) model, which spatially arranges them across the landscape to maximize connectivity while preserving farm-level profitability. Finally, we introduce a Bayesian Optimization (BO) approach that translates these spatial outcomes into simple, cost effective, and scalable policy instruments, such as subsidies and eco-premiums, using non-spatial, farm-level policy parameters. Applying the framework to a Canadian agricultural landscape, we demonstrate how it enhances connectivity under real-world economic constraints. Our approach provides a globally relevant tool for aligning farm incentives with biodiversity goals, advancing the development of agri-environmental policies that are economically viable and ecologically effective.

cs.CE↗

arXiv2025

Climate benefits of afforestation and reforestation with varying species mixtures and densities in the north-western boreal lands

The boreal forest plays a crucial role as a global carbon sink. This study uses two 250-year simulations of Canada's Taiga Plains, an area targeted by the 2 Billion Trees Program to evaluate afforestation and reforestation strategies that vary by species mix, planting density, and surface albedo. Medium density stands, 600 to 1400 trees per hectare, composed of mixed species with approximately 25 to 40 percent deciduous trees sequestered 15 to 30 percent more net ecosystem carbon than conifer monocultures. These benefits stem from a combination of rapid early growth, long-term carbon retention, and enhanced resilience to disturbance. Replanting understocked stands with such mixtures increased long-term carbon storage by 18 to 30 percent relative to prevailing scenarios. When surface albedo was considered, pure evergreen or deciduous stands showed a reduction in climate benefit by 6 to 20 percent, while mixed stands maintained net cooling and achieved the highest sequestration rates, approximately 4.6 to 4.7 tons of carbon dioxide equivalent per hectare per year. Scenarios involving partial harvesting followed by replanting sustained or improved ecosystem carbon stocks, about 300 to 340 tons of carbon per hectare, and productivity, roughly 1.6 to 2.0 tons of carbon per hectare per year, without increasing ecological risk. Overall, integrating fast-growing deciduous species with long-lived conifers at moderate planting densities enhances the climate mitigation potential of boreal afforestation and reforestation efforts and offers guidance for reforestation policy in similar high latitude ecosystems.

q-bio.PE↗

arXiv2025

Carbon removal capacity estimation of taiga reforestation and afforestation at the western boreal edge using spatially explicit carbon budget modeling

Canada's northern boreal forest edge offers considerable potential for climate change mitigation through large-scale tree planting. Afforestation in these sparsely forested regions could assist the natural northward migration of forests while capitalizing on their carbon removal capacity. However, the sequestration potential is uncertain due to a lack of spatially explicit models. This study uses Monte Carlo estimates and a carbon budget model to quantify the carbon removal capacity of afforestation at the northwestern boreal edge from 2025 to 2100. We combined satellite inventory data and probabilistic fire regime representations to simulate total ecosystem carbon under scenarios considering fire return intervals, land classes, planting mortality, and climate variables. Our results indicate that afforesting ~6.4-32 million hectares could sequester ~3.88-19.4 Gigatonnes of $CO_{2}$e over the next 75 years, with the Taiga Shield West ecozone showing the most potential. Even the conservative estimate (3.88 Gt$CO_{2}$e) is over five times Canada's total annual greenhouse gas emissions, making it a substantial contribution toward the nation's 2050 net-zero goal. Further research is needed to refine these estimates, assess economic viability, and investigate impacts on regional processes like permafrost thaw and surface albedo.

physics.comp-ph↗

arXiv2024

Assessing the climate benefits of afforestation: processes, methods, and frameworks

Afforestation greatly influences several earth system processes, making it essential to understand these effects to accurately assess its potential for climate change mitigation. Although our understanding of forest-climate system interactions has improved, significant knowledge gaps remain, preventing definitive assessments of afforestation's net climate benefits. In this review, focusing on the Canadian northern boreal and southern arctic, we identify these gaps and synthesize existing knowledge. The review highlights regional realities, Earth's climatic history, uncertainties in biogeochemical (BGC) and biogeophysical (BGP) changes following afforestation, and limitations in current assessment methodologies, emphasizing the need to reconcile these uncertainties before drawing firm conclusions about the climate benefits of afforestation. Finally, we propose an assessment framework which considers multiple forcing components, temporal analysis, future climatic contexts, and implementation details. We hope that the research gaps and assessment framework discussed in this review inform afforestation policy in Canada and other circumpolar nations.

physics.ao-ph↗