Harry C. Wilting

There is an increasing demand for consistent methods and tools to quantify biodiversity footprints: the magnitude of biodiversity loss associated with all direct and indirect impacts associated with a given human activity or economic actor. Here, we present the intactness-based biodiversity impact factors (IBIF) dataset: a consistent set of country-level impact factors that can be used to attribute losses in local terrestrial biodiversity intactness to emissions and resource use associated with production or consumption in a given country. We used the GLOBIO biodiversity model and its mean species abundance (MSA) metric to obtain these impact factors for 234 countries and five environmental pressures: CO₂ emissions, NH₃ emissions, NO_x emissions, land use (urban land, cropland, pasture, forest plantations and mines) and roads. IBIF includes impact factors for vascular plants, warm-blooded vertebrates (birds & mammals) and both species groups combined. The dataset can be used to quantify the biodiversity footprints of current products, industrial sectors or consumers, in support of policy- and decision-making aimed at halting or reversing biodiversity loss.

Insights into subnational environmental impacts and the underlying drivers are scarce, especially from a consumption‐based perspective. Here, we quantified greenhouse gas (GHG) emissions and land‐based biodiversity losses associated with final consumption in 162 regions in the European Union in 2010. For this purpose, we developed an environmentally extended multi‐regional input–output (MRIO) model with subnational European information on demand, production, and trade structures subdivided into 18 major economic sectors, while accounting for trade outside Europe. We employed subnational data on land use and national data on GHG emissions. Our results revealed within‐country differences in per capita GHG and land‐based biodiversity footprints up to factors of 3.0 and 3.5, respectively, indicating that national footprints may mask considerable subnational variability. The per capita GHG footprint increased with per capita income and income equality, whereas we did not find such responses for the per capita land‐based biodiversity footprint, reflecting that extra income is primarily spent on energy‐intensive activities. Yet, we found a shift from the domestic to the foreign part of the biodiversity footprints with rising population density and income. Because our analysis showed that most regions are already net importers of GHG emissions and biodiversity losses, we conclude that it is increasingly important to address the role of trade in national and regional policies on mitigating GHG emissions and averting further biodiversity losses, both within and outside the region itself. To further increase the policy relevance of subnational footprint analyses, we also recommend the compilation of more detailed subnational MRIO databases including harmonized environmental data.

It is increasingly recognized that human consumption leads to considerable losses of biodiversity. This study is the first to systematically quantify these losses in relation to land use and greenhouse gas (GHG) emissions associated with the production and consumption of (inter)nationally traded goods and services by presenting consumption-based biodiversity losses, in short biodiversity footprint, for 45 countries and world regions globally. Our results showed that (i) the biodiversity loss per citizen shows large variations among countries, with higher values when per-capita income increases; (ii) the share of biodiversity losses due to GHG emissions in the biodiversity footprint increases with income; (iii) food consumption is the most important driver of biodiversity loss in most of the countries and regions, with a global average of 40%; (iv) more than 50% of the biodiversity loss associated with consumption in developed economies occurs outside their territorial boundaries; and (v) the biodiversity footprint per dollar consumed is lower for wealthier countries. The insights provided by our analysis might support policymakers in developing adequate responses to avert further losses of biodiversity when population and incomes increase. Both the mitigation of GHG emissions and land use related reduction options in production and consumption should be considered in strategies to protect global biodiversity.

Although multiregional input–output (MRIO) databases use data from national statistical offices, the reconciliation of various data sources results in significantly altered country data. This makes it problematic to use MRIO-based footprints for national policy-making. This paper develops a potential solution using the Netherlands as case study. The method ensures that the footprint is derived from an MRIO dataset (in our case the World Input–Output Database (WIOD)) that is made consistent with Dutch National accounts data. Furthermore, usage of microdata allows us to separate re-exports at the company level. The adjustment results in a foreign footprint in 2009 that is 22% lower than the original WIOD estimates and a significantly altered country allocation. We demonstrate that already in the data preparation phase due to the treatment of re-exports and margins, large differences arise with Dutch national statistics, which may help explain the variation in footprint estimates across MRIO databases.

Environmental multi-regional input–output (MRIO) models require large amounts of data that all have their specific uncertainties. This paper presents a sensitivity and uncertainty analysis in order to gain an understanding of the directions in which efforts should be made to reduce these uncertainties. The analyses were carried out for an MRIO model to calculate the Dutch carbon footprint. A sensitivity analysis of the technical coefficients showed that changes in the coefficients in the domestic blocks and in the Dutch import blocks had the largest effects on the calculated footprint. The uncertainty analysis consisting of a Monte Carlo simulation based on probability distributions around the model coefficients showed a relatively low degree of uncertainty in the total Dutch carbon footprint; uncertainties in the carbon emissions allocated to regions, sectors and products were larger. Both analyses showed that, in certain cases, it is justified to apply a partial MRIO analysis.