Biodiversity supports food security, clean water, climate stability, and livelihoods — particularly in developing countries. It underpins critical sectors such as agriculture, fisheries, ecotourism, and medicine, which are fundamental to poverty alleviation and sustainable growth, ensuring a livable planet. Yet global biodiversity is in crisis. According to the IPBES Global Assessment Report, extinction rates are now up to 1,000 times above natural levels, with nearly one million species at risk. The Living Planet Index shows a 68% decline in global biodiversity since 1970 — an alarming signal that urgent action is needed to protect the ecosystems vital to life and development. In response, 188 countries adopted the Kunming-Montreal Global Biodiversity Framework (GBF) at COP 15, including 30×30 goal to conserve 30% of the world’s land and oceans by 2030. Effective implementation of GBF requires integrating its principles into development sectors — particularly infrastructure.
To support GBF targets, biodiversity must be embedded into infrastructure policy, particularly in road development. Roads promote connectivity and growth but also fragment habitats, disturb hydrology and microclimates, and increase exposure to invasive species, pollution, and exploitation. Wildlife face habitat isolation, traffic mortality, and stress from noise and artificial lighting — reducing gene flow and long-term viability. Roads can also support environmental goals through wildfire detection and ecological monitoring if managed well. For infrastructure to contribute to both development and conservation, biodiversity data must inform planning, assessment, and design. Making ecological risk a core consideration in infrastructure decisions is essential for sustainable, inclusive growth.
A major obstacle in biodiversity-informed planning is lack of access to current, location-specific data — especially in developing regions where emerging threats often outpace slow, underfunded data cycles. Compounding the problem, traditional datasets focus narrowly on vertebrates, providing an incomplete picture of biodiversity. For instance, even World Bank’s research on ecological implications for road improvements in Bolivia, Cameroon, Democratic Republic of Congo, Lao PDR, and Myanmar published in Dasgupta and Wheeler 2022; Damania et al. 2018; Danyo, Dasgupta and Wheeler 2018 used IUCN and BirdLife International habitat maps. While useful to provide approximate baselines, these sources are limited in scope because they do not include all taxa — such as invertebrates, plants, fungi, and freshwater species— and data updates of species’ habitat can be infrequent. Meanwhile, threats to biodiversity continue to evolve rapidly. Recent advances in remote sensing, artificial intelligence, and machine learning now offer timely, high-resolution insights, allowing for stronger integration of biodiversity into infrastructure planning and risk mitigation.
The World Bank produces essential datasets that support sustainable development efforts. To address persistent biodiversity data gaps, the World Bank has recently developed a Global Species Database based on millions of georeferenced observations from the Global Biodiversity Information Facility (GBIF), processed using machine-based pattern recognition. Covering over 600,000 species — including plants, invertebrates, fungi, fish, and more — the database significantly improves both taxonomic and timeliness of the data. Building on this, we developed a scalable method to help planners identify biodiversity-critical road corridors. The system integrates multi-taxa species distribution data, leverages cloud computing for rapid updates, and overlays results with national road networks. This enables early identification of potential ecological risks in infrastructure projects — critical for aligning infrastructure planning with biodiversity conservation.
Our methodology classifies species into four priority groups based on species occurrence region size and endemism, meaning the species is found only within one country. The highest priority goes to endemic species with small occurrence region — most vulnerable to habitat loss. Global occurrence maps are generated by overlaying species data on a fine-resolution grid across 190 countries. For modeling road corridors, the approach uses OpenStreetMap road data, forest cover from Tuanmu and Jetz (2014), and topography from MERIT-DEM. Road links are classified by type, with forested areas including more categories to reflect sensitivity, and the selected links are buffered at 2.5 km on each side of the road. Steep areas are excluded to ensure construction feasibility. Species richness is then calculated within each corridor across priority groups. Results are standardized, color-coded, and comparable across regions — providing a decision-support tool for biodiversity-sensitive road planning.
Furthermore, it should be noted that our database and this methodology offers a general framework that users can adapt by selecting species of interest, applying their own criteria for identifying species at risk, and integrating local biodiversity as well as roads data to enhance relevance and precision.
Our Roads Database can be accessed at World Bank’s the Development Data Hub. For methodology and applications, see the following working paper.
Source : World Bank
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