Meeting the UN Sustainable Development Goals requires reconciling development with biodiversity conservation. Governments and lenders increasingly call for major industrial developments to offset unavoidable biodiversity loss but there are few robust evaluations of whether offset interventions ensure no net loss of biodiversity. We focus on the biodiversity offsets associated with the high-profile Ambatovy mine in Madagascar and evaluate their effectiveness at delivering no net loss of forest. As part of their efforts to mitigate biodiversity loss, Ambatovy compensate for forest clearance at the mine site by slowing deforestation driven by small-scale agriculture elsewhere. Using a range of methods, including extensive robustness checks exploring 116 alternative model specifications, we show that the offsets are on track to avert as much deforestation as was caused by the mine. This encouraging result shows that biodiversity offsetting can contribute towards mitigating environmental damage from a major industrial development, even within a weak state, but there remain important caveats with broad application. Our approach could serve as a template to facilitate other evaluations and so build a stronger evidence-base of the effectiveness of no net loss interventions.

Biodiversity offsetting is a globally influential policy mechanism for reconciling trade‐offs between development and biodiversity loss. However, there is little robust evidence of its effectiveness. We evaluated the outcomes of a jurisdictional offsetting policy (Victoria, Australia). Offsets under Victoria's Native Vegetation Framework (2002–2013) aimed to prevent loss and degradation of remnant vegetation, and generate gains in vegetation extent and quality. We categorised offsets into those with near‐complete baseline woody vegetation cover (“avoided loss”, 2702 ha) and with incomplete cover (“regeneration”, 501 ha), and evaluated impacts on woody vegetation extent from 2008 to 2018. We used two approaches to estimate the counterfactual. First, we used statistical matching on biophysical covariates: a common approach in conservation impact evaluation, but which risks ignoring potentially important psychosocial confounders. Second, we compared changes in offsets with changes in sites that were not offsets for the study duration but were later enrolled as offsets, to partially account for self‐selection bias (where landholders enrolling land may have shared characteristics affecting how they manage land). Matching on biophysical covariates, we estimated that regeneration offsets increased woody vegetation extent by 1.9%–3.6%/year more than non‐offset sites (138–180 ha from 2008 to 2018) but this effect weakened with the second approach (0.3%–1.9%/year more than non‐offset sites; 19–97 ha from 2008 to 2018) and disappeared when a single outlier land parcel was removed. Neither approach detected any impact of avoided loss offsets. We cannot conclusively demonstrate whether the policy goal of ‘net gain’ (NG) was achieved because of data limitations. However, given our evidence that the majority of increases in woody vegetation extent were not additional (would have happened without the scheme), a NG outcome seems unlikely. The results highlight the importance of considering self‐selection bias in the design and evaluation of regulatory biodiversity offsetting policy, and the challenges of conducting robust impact evaluations of jurisdictional biodiversity offsetting policies.

Biodiversity offsetting is a globally-influential policy mechanism for reconciling trade-offs between development and biodiversity loss. However, there is little robust evidence of its effectiveness. We evaluated the outcomes of a jurisdictional offsetting policy (Victoria, Australia). Offsets under Victoria’s Native Vegetation Framework (2002-2013) aimed to prevent loss and degradation of remnant vegetation, and generate gains in vegetation extent and quality. We categorised offsets into those with near-complete baseline woody vegetation cover (“avoided loss”, 2702 ha) and with incomplete cover (“regeneration”, 501 ha), and evaluated impacts on woody vegetation extent from 2008-2018. We used two approaches to estimate the counterfactual. First, we used statistical matching on biophysical covariates: a common approach in conservation impact evaluation, but which risks ignoring potentially important psychosocial confounders. Second, we compared changes in offsets with changes in non-offset sites that were later enrolled as offsets, to partially account for self-selection bias (where landholders enrolling land may have characteristics associated with different management). Matching on biophysical covariates, we estimated that regeneration offsets increased woody vegetation extent by 1.9-3.6%/year more than non-offset sites (138-180 ha from 2008-2018), but this effect weakened with the second approach (0.3-1.9%/year more than non-offset sites; 19-97 ha from 2008-2018) and disappeared when a single outlier land parcel was removed. Neither approach detected any impact of avoided loss offsets. We cannot conclusively demonstrate whether the policy goal of ‘net gain’ was achieved because of data limitations. However, given our evidence that the majority of increases in woody vegetation extent were not additional (would have happened without the scheme), a net gain outcome seems unlikely. The results highlight the importance of considering self-selection bias in the design and evaluation of regulatory biodiversity offsetting policy. They also highlight the challenges of conducting robust impact evaluations of jurisdictional biodiversity offsetting policies.

Significance StatementKey differences exist between how rural and urban people receive benefits from nature (termed ecosystem services; ES). In rural areas, people are thought to have relatively direct relationships with local ecosystems (e.g. growing food on your subsistence farm). By contrast, within urban areas, people often have more indirect access to distant ecosystems (e.g. obtaining food from hundreds of miles away via supermarket value chain). However, this leaves many questions unanswered: e.g., What natural benefits are present within cities? When do nature’s benefits flow into cities? When do the people travel out to directly receive nature’s benefits? Here, we explore this issue – breaking down ES flows into two components (i.e. the movement of natural goods and the movement of beneficiaries [people]).

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