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Tropical forests support immense biodiversity and provide essential ecosystem services for billions of people. Despite this value, tropical deforestation continues at a high rate. Emerging evidence suggests that elections can play an important role in shaping deforestation, for instance by incentivising politicians to allow increased utilisation of tropical forests in return for political support and votes. Nevertheless, the role of elections as a driver of deforestation has not been comprehensively tested at broad geographic scales. Here, we created an annual database from 2001 to 2018 on political elections and forest loss for 55 tropical nations and modelled the effect of elections on deforestation. In total, 1.5 million km2 of forest was lost during this time period, and the rate of deforestation increased in 37 (67%) of the analysed countries. Deforestation was significantly lower in years with presidential or lower chamber elections compared to non-election years, which is in contrast to previous local-scale studies. Moreover, deforestation was significantly higher in presidential or lower chamber elections that are competitive (i.e. when the opposition can participate in elections and has a legitimate chance to gain governmental power) compared to uncompetitive elections. Our results document a pervasive loss of tropical forests and suggest that competitive elections are potential drivers of deforestation. We recommned that organisations monitoring election transparency and fairness should also monitor environmental impacts such as forest loss, habitat destruction and resource exploitation. This would benefit the tracking of potential illegal vote buying with natural resources.

Green infrastructure (GI) classifications are widely applied to predict and assess its suitability for urban biodiversity and ecosystem service (ES) provisioning. However, there is no consolidated classification, which hampers elucidating synthesis and consolidated relationships across ES and biodiversity.In this research, we aim to bridge the gap between urban GI research on ES and biodiversity by providing a standardized common classification that enables consistent spatial analysis.We analyzed GI classifications used across five ES and four taxa in scientific literature. GI classes were analyzed based on name, definition and characteristics. Results were used to create a novel classification scheme accounting for both ES and biodiversity.We show that many GI classes are unique to a ES or taxon, indicating a lack of multifunctionality of the classification applied. Among the universally used classes, diversity in their definitions is large, reducing our mechanistic understanding of multifunctionality in GI. Finally, we show that most GI classes are solely based on land-use or land-cover, lacking in-depth detail on vegetation. Through standardization and incorporation of key characteristics, we created a consolidated classification. This classification is fully available through openly-accessible databases.Our consolidated standardized classification accommodates interdisciplinary research on ES and biodiversity and allows elucidating urban biodiversity and ES relationships into greater detail, facilitating cross-comparisons and integrated assessments. This will provide a foundation for future research efforts into GI multi-functionality and urban greening policies.HighlightsUniversally used GI classes have a vast variety in definitionsUnique GI classes may indicate specific mechanisms and lack of multifunctionalityCurrent GI characteristics are insufficient for mechanistic understandingA GI classification is provided to support combined biodiversity and ES researchOur classification provides a foundation for future GI research