Economics & Development

The expansion of marine protected areas (MPAs) is a core focus of global conservation efforts, with the “30x30” initiative to protect 30% of the ocean by 2030 serving as a prominent example of this trend. This paper examines a series of proposed MPA network expansions of various sizes and forecasts the impact that increased protection could have on global patterns of fishing effort. This is accomplished using a predictive machine learning model trained on a global dataset of satellite-based fishing vessel monitoring data, current MPA locations, and spatiotemporal environmental, geographic, political, and economic features. The model predicts future fishing effort under various MPA expansion scenarios, compared to a business-as-usual counterfactual scenario that includes no new MPAs. The difference between these scenarios represents the predicted change in fishing effort resulting from MPA expansion. The results show that, regardless of the MPA network's objective or size, fishing effort would decrease inside the MPAs, though by much less than 100%. Moreover, this reduction in fishing effort within MPAs does not simply shift outside—fishing effort outside MPAs also declines. The overall magnitude of the predicted decrease in global fishing effort principally depends on where networks are placed in relation to existing fishing effort. MPA expansion will lead to a global redistribution of fishing effort, which should be considered in network design, implementation, and impact evaluation.