Biodiversity Impact Assessment Considering Land Use Intensities and Fragmentation.

Land use is a major threat to terrestrial biodiversity. Life cycle assessment is a tool that can assess such threats and thereby support environmental decision-making. Within the Global Guidance for Life Cycle Impact Assessment (GLAM) project, the Life Cycle Initiative hosted by UN Environment aims to create a life cycle impact assessment method across multiple impact categories, including land use impacts on ecosystem quality represented by regional and global species richness. A working group of the GLAM project focused on such land use impacts and developed new characterization factors to combine the strengths of two separate recent advancements in the field: the consideration of land use intensities and land fragmentation. The data sets to parametrize the underlying model are also updated from previous models. The new characterization factors cover five species groups (plants, amphibians, birds, mammals, and reptiles) and five broad land use types (cropland, pasture, plantations, managed forests, and urban land) at three intensity levels (minimal, light, and intense). They are available at the level of terrestrial ecoregions and countries. This paper documents the development of the characterization factors, provides practical guidance for their use, and critically assesses the strengths and remaining shortcomings.

China's dietary changes would increase agricultural blue and green water footprint.

Agriculture accounts for 61 % of fresh water consumption in China. Although population and diet have a significant impact on water consumption, little is known about the reasons for and extent of their influence. Changes in the blue and green water footprint of 20 agricultural sectors in 31 Chinese provinces were estimated in 5 scenarios by applying the environmentally expanded multi-regional input-output model. The water footprint network is strongly interconnected, with over 50 % of the provinces characterized as net importers of the blue water footprint, 70 % of the total blue and green water footprint imports in developed provinces, and 65 % of the total blue and green water footprint exports in developing provinces, with the flow distribution driven and dominated by economically developed provinces. The findings also highlighted that the impact of population change on the water footprint is insignificant, contributing 0.51 % and 5.78 % to the reduction of the water footprint in 2030 and 2050, respectively. The impact of simultaneous changes in the population and dietary structure on the water footprint was higher than population changes and lower than dietary structure changes. The main force driving changes in the water footprint was changes in the dietary structure, which resulted in a two-fold effect on the water footprint. First, it has increased the blue and green water footprint by 33 % and 12 %, respectively, thus aggravating the coercive impact on water resources on the production side. Second, it has led to a change in the main contributing sectors for the blue and green water footprint from cereals to fruits, vegetables, and potatoes. Therefore, when the population is changing and optimizing its dietary structures, a greater focus must be placed on threats and pressures to water resources. This will result in better scientific management and more efficient use of water resources.

Global Human Consumption Threatens Key Biodiversity Areas.

Key biodiversity areas (KBAs) are critical regions for preserving global biodiversity. KBAs are identified by their importance to biodiversity rather than their legal status. As such, KBAs are often under pressure from human activities. KBAs can encompass many different land-use types (e.g., cropland, pastures) and land-use intensities. Here, we combine a global economic model with spatial mapping to estimate the biodiversity impacts of human land use in KBAs. We find that global human land use within KBAs causes disproportionate biodiversity losses. While land use within KBAs accounts for only 7% of total land use, it causes 16% of the potential global plant loss and 12% of the potential global vertebrate loss. The consumption of animal products accounts for more than half of biodiversity loss within KBAs, with housing the second largest at around 10%. Bovine meat is the largest single contributor to this loss, at around 31% of total biodiversity loss. In terms of land use, lightly grazed pasture contributes the most, accounting for around half of all potential species loss. This loss is concentrated mainly in middle- and low-income regions with rich biodiversity. International trade is an important driver of loss, accounting for 22-29% of total potential plant and vertebrate loss. Our comprehensive global, trade-linked analysis provides insights into maintaining the integrity of KBAs and global biodiversity.

Adoption of plant-based diets across Europe can improve food resilience against the Russia-Ukraine conflict.

Crises related to extreme weather events, COVID-19 and the Russia-Ukraine conflict have revealed serious problems in global food (inter)dependency. Here we demonstrate that a transition towards the EAT-Lancet's planetary health diet in the European Union and the United Kingdom alone would almost compensate for all production deficits from Russia and Ukraine while yielding improvements in blue water use (4.1 Gm3 yr-1), greenhouse gas emissions (0.22 GtCO2e yr-1) and carbon sequestration (17.4 GtCO2e).

Dietary change in high-income nations alone can lead to substantial double climate dividend.

A dietary shift from animal-based foods to plant-based foods in high-income nations could reduce greenhouse gas emissions from direct agricultural production and increase carbon sequestration if resulting spared land was restored to its antecedent natural vegetation. We estimate this double effect by simulating the adoption of the EAT-Lancet planetary health diet by 54 high-income nations representing 68% of global gross domestic product and 17% of population. Our results show that such dietary change could reduce annual agricultural production emissions of high-income nations' diets by 61% while sequestering as much as 98.3 (55.6-143.7) GtCO2 equivalent, equal to approximately 14 years of current global agricultural emissions until natural vegetation matures. This amount could potentially fulfil high-income nations' future sum of carbon dioxide removal (CDR) obligations under the principle of equal per capita CDR responsibilities. Linking land, food, climate and public health policy will be vital to harnessing the opportunities of a double climate dividend.

Going Global to Local: Connecting Top-Down Accounting and Local Impacts, A Methodological Review of Spatially Explicit Input-Output Approaches.

Environmentally Extended Input-Output Databases (EEIOs) provide an effective tool for assessing environmental impacts around the world. These databases have yielded many scientific and policy relevant insights, especially through the national accounting of impacts embodied in trade. However, most approaches average out the spatial variation in different factors, usually at the level of the nation, but sometimes at the subnational level. It is a natural next step to connect trade with local environmental impacts and local consumption. Due to investments in earth observation many new data sets are now available, offering a huge potential for coupling environmental data sets with economic models such as Multi-Region Input-Output (MRIO) models. A key tool for linking these scales are Spatially Explicit Input-Output (SIO) models, which provide both demand and supply perspectives by linking producers and consumers. Here we define an SIO model as a model having a resolution greater than the underlying input-output transaction matrix. Given the increasing interest in this approach, we present a timely review of the methods used, insights gained, and limitations of various approaches for integrating spatial data in input-output modeling. We highlight the evolution of these approaches, and review the methodological approaches used in SIO models so far. We investigate the temporal and spatial resolution of such approaches and analyze the general advantages and limitations of the modeling framework. Finally, we make suggestions for the future development of SIO models.