Greenhouse gas emissions assessment and mitigation potential in Pakistan's cattle and Buffalo sector: An IPCC tier 2 inventory method with probabilistic uncertainty modeling

Context Livestock methane reduction is central to achieving global climate goals, yet most developing countries lack spatially explicit and uncertainty-quantified greenhouse gas (GHG) inventories needed for transparent monitoring, reporting and verification (MRV) under the Paris Agreement. Pakistan's cattle and buffalo sector, contributing about 22.5% of national GHG emissions, lacks spatially explicit and uncertainty-quantified inventories needed for targeted policy design and transparent monitoring. Objective The objectives of this research are threefold: (1) to develop a high-resolution, spatially explicit Tier 2 livestock GHG inventory for Pakistan's cattle and buffalo sector using 2022 as the baseline year; (2) to quantify and map uncertainty in emission estimates through Monte Carlo simulation, producing the first probabilistic livestock GHG inventory for Pakistan; and (3) to assess the mitigation potential of plausible interventions under 2030 projections, identifying priority regions and interventions; and contribute a methodological framework for other developing countries seeking to advance beyond Tier 1 inventories. Methods This study introduces a replicable IPCC Tier 2 framework that integrates district-level disaggregation, probabilistic uncertainty analysis, and mitigation scenario assessment, demonstrated through Pakistan's cattle and buffalo sector which contributes approximately 22.5% of national GHG emissions. The inventory quantifies methane (CH₄) from enteric fermentation and manure, and nitrous oxide (N₂O) from manure management, using region-specific parameters, district herd projections, and Monte Carlo simulations, excluding CO₂ and fertilizer-related emissions. Results and conclusions National emissions were estimated at 172.18 Mt. CO₂e yr −1 in 2022 and projected to reach 195.14 Mt. CO₂e yr −1 in 2030 (+13.34%), with enteric CH₄ contributing approximately 86.8% of total emissions. IFE produced the largest national reduction (6.84 Mt. CO₂e yr −1 ; 3.50%), followed by RG (0.20 Mt. CO₂e yr −1 ; 0.10%) and MTB (0.067 Mt. CO₂e yr −1 ; 0.035%). The 2030 BAU probabilistic mean was 195.14 Mt. CO₂e yr −1 (95% uncertainty interval: 174.94–215.60), and mitigation-scenario uncertainty intervals overlapped substantially. Significance Through the integration of spatially explicit inventories, probabilistic uncertainty analysis and mitigation scenario modeling, this framework strengthens MRV systems, advances NDC and methane pledge implementation and provides a scalable model for livestock-intensive developing countries.