# The biodiversity-climate-food nexus: Illustrating challenges and solutions using the Green Shoots framework

Almut Arneth, Peter Alexander, Richard Fuchs, Yunne-Jai Shin, Joachim

Claudet, Marta Coll, Paul Leadley, Carlo Rondinini, Mark Rounsevell

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## To cite this version:

Almut Arneth, Peter Alexander, Richard Fuchs, Yunne-Jai Shin, Joachim Claudet, et al. The biodiversity-climate-food nexus: Illustrating challenges and solutions using the Green Shoots framework. One Earth, 2025, 8 (11), pp.101455. 10.1016/j.oneear.2025.101455. hal-05385689

HAL Id: hal-05385689

https://hal.science/hal-05385689v1

Submitted on 27 Nov 2025

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Review

# The biodiversity-climate-food nexus: Illustrating challenges and solutions using the Green Shoots framework

Almut Arneth,<fnref n="1" />,<fnref n="8" />,* Peter Alexander,<fnref n="2" /> Richard Fuchs,<fnref n="1" /> Yunne-Jai Shin,<fnref n="3" /> Joachim Claudet,<fnref n="4" /> Marta Coll,<fnref n="5" />,<fnref n="6" /> Paul Leadley,<fnref n="7" /> Carlo Rondinini,<fnref n="8" /> and Mark Rounsevell<fnref n="1" />,<fnref n="2" />,<fnref n="9" />

## SUMMARY

Responding to the twin challenges of climate change and biodiversity loss is critical but so is avoiding any unintended consequences of these responses for other sustainability goals such as food security. Here, we explore synergies and trade-offs across the biodiversity-climate-food nexus through the recently developed Green Shoots framework. This approach highlights how different response options (dietary change, sustainable food production and fisheries, afforestation/reforestation, and bioenergy) can have multiple co-benefits, although some can also pose risks to biodiversity, climate change, or food security. Integrated responses are needed, but ultimately, success depends on how effectively they are implemented, and not on the type of response option per se. Society is at a critical juncture for the sustainability of most natural and food systems with today's choices affecting tomorrow's outcomes, and the Green Shoots provide an approach to inform these choices.

## INTRODUCTION

Anthropogenic greenhouse gas emissions have resulted in global mean warming of 1.2°C above the pre-industrial period, with emissions increasing every year. Considering the remaining carbon budget, the 1.5°C target put forward in the Paris Agreement is likely no longer achievable.<fnref n="1" /> Humanity is thus on a path to levels of warming considered to be dangerous anthropogenic interference with the climate system,<fnref n="2" /> nullifying the primary objective of the United Nations Framework Convention on Climate Change (UNFCCC). Concurrently, more species than ever before are threatened with extinction from anthropogenic causes, two-thirds of the world's ocean and three-quarters of the ice-free land area are under direct anthropogenic use at different levels of intensity, and between one and six billion ha of land may be severely degraded.<fnref n="3" />⁻<fnref n="5" /> Degradation is also widely identified in many marine ecosystems.<fnref n="6" />,<fnref n="7" /> This direct use of land and seas is dominated by the need to feed the world's growing population. However, there are more than 800 million people undernourished today, with an increasing tendency,<fnref n="8" /> making the supply of sufficient and nutritious food one of the foremost societal challenges. Clearly, humans are interfering dangerously not only with the climate system but also with socio-ecological systems.

The evidence is overwhelming that intact biodiversity and well-functioning ecosystems are required to mitigate (and adapt to) climate change and to achieve many of the Sustainable Development Goals (SDGs) beyond “life on land” and “life below water,” including “zero hunger.”<fnref n="4" />,<fnref n="9" />,¹⁰ But likewise, biodiversity targets such as those of the Convention on Biological Diversity’s (CBD’s) Kunming-Montréal Global Biodiversity Framework can only be achieved if climate change is halted and feeding the world’s population is decoupled from resource over-exploitation.¹⁰⁻¹² International and national climate and biodiversity policies increasingly acknowledge the close interdependencies of climate change and biodiversity loss and the need to closely align targets specified under the UNFCCC and CBD. Nevertheless, the agreed targets, but also the measures put in place to meet these targets, do not fully acknowledge the complex, non-linear interactions that exist in socio-ecological systems, thus risking failure because of environmental or societal trade-offs.<fnref n="9" />,¹¹,¹²

Successful implementation of policy measures requires an integrated perspective that acknowledges the manifold direct and indirect drivers operating within the biodiversity-climate-food nexus.¹³⁻¹⁵ We focus here on a range of widely discussed response options that either aim to contribute to climate

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<footnote n="1">¹KIT, Department of Atmospheric Environmental Research, 82467 Garmisch-Partenkirchen, Germany</footnote>

<footnote n="2">²School of Geosciences, University of Edinburgh, Drummond Street, EH8 9XP Edinburgh, UK</footnote>

<footnote n="3">³IRD, Univ. Montpellier, IFREMER, CNRS, MARBEC, Place E. Bataillon, CC 093, 34095 Montpellier Cedex 5, France</footnote>

<footnote n="4">⁴CNRS, PSL-EPHE-UPVD, CRIOBE, Maison de l'Océan, 195 rue Saint-Jacques, 75005 Paris, France</footnote>

<footnote n="5">⁵Institute of Marine Science (ICM-CSIC), Passeig Marítim de la Barceloneta, 08003 Barcelona, Spain</footnote>

<footnote n="6">⁶Ecopath International Initiative (EII) Research Association, Barcelona, Spain</footnote>

<footnote n="7">⁷ESE Laboratory, Université Paris-Saclay/CNRS/AgroParisTech, 91400 Orsay, France</footnote>

<footnote n="8">⁸Global Mammal Assessment Program, Department of Biology and Biotechnologies, Sapienza University of Rome, 00151 Rome, Italy</footnote>

<footnote n="9">⁹KIT, Department of Geography and Geoecology, Karlsruhe, Germany</footnote>

<footnote>*Correspondence: almut.arneth@kit.edu</footnote>

<footnote>https://doi.org/10.1016/j.oneear.2025.101455</footnote>
change mitigation (especially through measures taken in land ecosystems) and/or underpin the supply of food. Response options include, for example, dietary change, sustainable food production, afforestation/reforestation, and bioenergy. They have been critically discussed previously regarding the co-benefits and negative side effects for biodiversity and human well-being (e.g.,¹⁰,¹⁶,¹⁷ and references therein). Here, we use the available literature, and—where available—country statistics, to assess these options within the “Green Shoots” framework.¹⁸ This framework was previously used to visualize the synergies and trade-offs arising from protected areas and their impacts on biodiversity, climate change mitigation, and food production.¹⁸ Our previous work showed that area coverage targets such as protecting 30% of the marine and land surface, a core element of the Kunming-Montréal Global Biodiversity Framework, are meaningless unless accompanied by substantial improvements in protected-area effectiveness. Moreover, important differences in terrestrial and marine systems need to be recognized when setting and implementing protected-area targets, regarding both synergies and trade-offs with climate change mitigation and food production.¹⁸ Here, we apply the Green Shoots visualization to a biodiversity-climate-food nexus perspective and explore a wider set of response options (beyond the initially investigated protected areas) that allow us to identify the opportunities that exist for achieving synergies across the nexus. The Green Shoots were inspired by the “burning embers” diagrams¹⁹,²⁰ of the Intergovernmental Panel on Climate Change but rather than communicating climate-change related risks, the Green Shoots emphasize options for solutions and contrast synergistic opportunities with actions that risk dangerous levels of anthropogenic interference in socio-ecological systems.

## METHODS

The Nature's Green Shoots visualization with its solutions-oriented analytical framework aims to identify possible sets of actions toward achieving sustainability objectives in the use of natural resources, as well as positive synergies but also trade-offs that may be associated with these actions. The Green Shoots are represented as a 2D surface that contrasts a response option to address a sustainability challenge (here, biodiversity loss, climate change, and hunger) with the effectiveness with which that option is implemented. A detailed description of the method is provided in Arneth et al.¹⁸ Briefly, a color scale from gray (a delay of an action, or even the action itself, is harmful) to green (action has clear benefits) identifies the solutions space. Responses of biodiversity, climate, or food production to an intervention can be linear or non-linear (for example, due to feedbacks in socio-ecological systems), and hence the color transitions from gray to green can also be linear or non-linear. Each Green Shoot diagram was developed in a separate worksheet (see the examples in Data S1, which is provided in the supplemental information). In each of the diagrams, the full color-range from dark gray to dark green is used, i.e., the implemented actions result in maximum unfavorable or favorable outcomes, which facilitates qualitative comparison between the Green Shoots. In these worksheets, the darkest green and gray shades correspond to values of -100 and +100, which enables subsequent

re-drawing and smoothing of the surface of the Green Shoots (see Arneth et al.¹⁸ for more details).

The assessment of the benefits or disadvantages arising from an intervention and hence also the choice of where the color transitions lie on the Shoots diagrams requires expert judgment, based on a review and evaluation of the published scientific literature. In some cases, the placement of the color transitions can also be based on available statistical data. For instance, in the analysis presented in this review, the impact of changes in (agricultural) food consumption and production was assessed with support from country-based data (see sub-sections “food production on land” and “(agricultural) food consumption” below). The vertical dimension (y axis) of the Green Shoots represents possible interventions that relate to policy targets and related indicators. Its scale ranges from none (or a very small) realization of an intervention to a maximum value, expressed in %. The solutions perspective of the Green Shoots diagrams also requires a second dimension (here plotted along the x axes), which captures the effectiveness of implementation. The level of uncertainty in color attribution increases with departure from current levels. Figure 1 provides a “generic” example of a Green Shoot as an illustration for how these should be read. The arrows and numbers on each Shoot refer the reader to concrete examples that we discuss in the respective sections in the context of each response option; these examples tend to be independent from one another.

We explore direct climate change mitigation measures (growth of bioenergy crops, expansion of area under forest, reduction of greenhouse gas emissions from agriculture), the impacts of fisheries, and dietary changes on biodiversity, climate, and food supply. These measures have been debated for many years regarding how well they address current unsustainable land and sea use, in particular (for the land-based measures) with respect to their potential to reduce the global “land-squeeze.”²¹ They have featured prominently in assessment reports by the IPCC (e.g., 2019⁵ and 2022²²,²³) and Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES; e.g., IPBES, 2019,⁴ and McElwee et al.²⁴) and a joint IPCC-IPBES workshop.⁹,¹⁷,²⁵,²⁶ The main outcomes of these reports and the literature that underpins them, plus more recent literature and some country-level statistics, are the basis for this paper, and the Green Shoots visualizations (presented in Figures 2 and 3; see also supplemental information, Tables S1.1–S1.5).

Figures 2 and 3 are based on the templates provided in Data S1, which show the assumptions made for the transitions. The surfaces created in these worksheets were replotted and smoothed using R 4.3.1 packages sp, gstat, ggplot2, and autopmap using the *autokriege* and *kriege* (for Figure 2D, climate, and Figure 3B, climate) functions. The interpretation of the impact of different solutions is done here implicitly for present-day conditions (i.e., not considering future socio-economic changes) and in the global context, while recognizing that synergies and trade-offs arising from a response option will differ between regions and geographic scales. In principle, the spreadsheet-based approach also allows for a wider set of variables to be explored, including those at the regional scale, or for multiple socio-economic and climate change scenarios. Biodiversity is intended to cover all facets of biodiversity, but most of the cited
**Sustainable Development Objective**
Biodiversity, climate change or food

<figure><img src="image_4.png" /><figcaption>**Figure 1.** Green Shoots visualization</figcaption></figure>

The green-to-gray color scale was chosen to illustrate overall favorable (green) or unfavorable (gray) impact a response option would have for sustainability challenges (here: biodiversity, climate change mitigation, or food supply). The vertical dimension (y axis) represents the degree of implementation of the proposed option, typically varying between a minimum of 0% to implementing the intervention to a maximum of 100%. The horizontal dimension (x axis) ranges from low to high level of effectiveness with which the intervention is implemented, “low” and “high” corresponding typically to an index ranging from zero to one. The current status is indicated by the encircled “c” in the x-y space. Numbers (1, 2, ...) represent different possible futures (discussed in the text) that illustrate the impact a change in the magnitude of the intervention and/or its effectiveness would have (possible futures). The arrows are included to guide the eye from the current status to each of the possible futures. The location of color transitions (gray to green), i.e., the degree of intervention at which the response becomes favorable or unfavorable, is uncertain, indicated by the different shading of the arrows and background numbers. In this example, strengthening the response option at unchanged effectiveness would be favorable with low uncertainty. In contrast, a decline in both effectiveness and the response option itself (possible future case 2) would be unfavorable, but with medium uncertainty regarding the strength and speed with which this would materialize. The Shoot example shown here can be combined with different panels to create one figure. Redrawn from Arneth et al.¹⁸

literature we used here refers to species diversity; climate change mitigation through actions in land ecosystems refers mainly to impacts on carbon pools and uptake, although for agriculture some of the cited studies refer also to other greenhouse gas emissions; food relates to quantity of food produced or food protein consumed.¹⁸

## Food production on land

Food production can be increased in two ways: through intensification (including technology changes) and through cropland area expansion.²⁷ We focused on data for the cropland sector, which integrates over food for direct human consumption and animal feed; given the greater trade-off between expansion of area and fertilizer use for cropland compared to pasture, intensity of production on pastures was not considered.

Cropland production intensification was estimated from national 5-year averages in fertilizer application rates from FAOSTAT for 2017–2021 (http://www.fao.org/faostat/en/#home). Fertilizer application rates are expected to correlate with other yield-enhancing factors such as pesticides and herbicides, irrigation, and mechanized land management. After excluding outliers, we normalized (0–1) for the y axis values between the minimum and maximum values of all countries, such that unity (expressed as 100%) would be the country currently with the highest fertilizer input rate.

National production efficiencies were used to define the x axis, as the attainable yield (1) minus the yield gap.²⁸ This approach is often used for assessing opportunities to increase the agricultural productivity or vice-versa to estimate the amount of in-efficiencies in a production system.²⁹

Cropland area expansion as a percentage of the total national area of a country was quantified for the period 2000–2021 (http://www.fao.org/faostat/en/#home). Similar to the normalization of intensification values, we normalized cropland expansion between the minimum and maximum values of all countries to improve comparability across countries and between area expansion and intensification, with 0% expansion having a normalized value of 0.5.

For details of these estimates see the additional information provided in the supplemental information. The color scale set in Figures 2A and 2B draws on these and is further refined based on the literature review.

## Fisheries

We focus here on marine capture fisheries, including both industrial and small-scale fisheries, which extract wild fish and invertebrate species from their natural environment. We chose to address the sustainability of fisheries’ seafood production through their impact on marine biodiversity (i.e., fish biomass) and fishery yield (i.e., catch). The assessment is based on key scientific reports and papers (see Table S1.2) that use fishery data reconstruction, scientific survey data, meta-analyses as well as single stock assessments. In the marine Green Shoots, the vertical dimension represents the proportion of sustainably exploited stocks, a common indicator (e.g., Shin et al.³⁰) calculated to track whether “the impacts of fisheries on stocks, species, and ecosystems are within safe ecological limits” (Aichi Target 6 of the CBD), and whether fish stocks are restored “at least to levels that can produce maximum sustainable yield” (SDG 14.4). A stock is considered sustainably fished when fishing mortality (F) is kept below a level that produces the maximum sustainable yield ($F_{MSY}$). The vertical dimension is scaled between 0 (none of the fish stocks are sustainably exploited) to 1 (all stocks are sustainably exploited). The horizontal dimension (x axis) provides a measure of the effectiveness of fisheries management. We chose to reflect the efficiency in combatting illegal,
unreported, and unregulated (IUU) fisheries, as the high propor-
tion of IUU fishing is a main driver of overexploitation and stands
in the way of efforts to rebuild depleted stocks. ³¹ Preventing, de-
terring, and eliminating IUU fishing is the main objective of the
FAO Agreement on Port State Measures, a target of the UN
SDG 14.4 and relates also to target 5 of the CBD GBF. The x
axis is scaled between 0 (minimum effectiveness, corresponding
to the upper estimate of IUU fishing risk index) and 1 (lower es-
timate of IUU fishing risk index). ³² Details of the method are
provided in the supplemental information. The color scale set
in Figure 2C draws on these and is further refined based on
our literature review.

(Agricultural) food consumption

Food consumption was analyzed across a two-dimensional sur-
face representing the fraction of plant-based foods in the diet on
the y axis and a metric of consumer efficiency on the x axis (see
Figure S3). Points on this surface represent food consumption pat-
terns in terms of quantity and types of foods. Consumption effi-
ciency is calculated as a required average per capita intake of
52 g protein/day³³,³⁴ divided by an average per capita food protein
supplied (comprising food eaten as well as discarded food waste),
using data from FAOSTAT³⁵ for individual countries. The term
"effectiveness" is used across all Green Shoots' horizontal dimen-
sions and was also applied in the context of food consumption
since efficient processes contribute significantly to effectiveness.
A consumption efficiency of 1 (100%) implies the food supply
exactly provides the required protein to meet nutritional require-
ments for the whole population if equitably distributed and with
no discarded food. A value of less than 100% indicates on average
an oversupply of food to the consumer, in comparison to nutritional
requirements. This could be either in the form of consumer food
losses or an individual's over-consumption, given that overeating
is a form of food waste. ³³,³⁶ Values of consumption efficiencies
above 1 (or 100%) indicate insufficient supply of protein, while a
value of 100% (or below) can also represent a case where food
is not equitably distributed, and potentially including undernourish-
ment for some. To address this, adjusted efficiencies were calcu-
lated based on FAO data for the number of people undernourished
per country. ³⁷

The vertical dimension represents the percentage of protein
from plant-based foods in a countries’ average diet. Commodity
protein contents were obtained from FAO food supply,³⁵ with a
calculated range of 30% to >90% plotted as an index (0-1).
For details on these estimates see the additional information
provided in the supplemental information. The color scale set
in Figure 2D draws on these and is further refined based on the
literature review.

Afforestation/reforestation

The y axis was scaled between 0 (complete deforestation) and 1
(maximum natural forest area, 50–55 km² ²⁷,³⁸). Effectiveness
was assessed based on the degree of naturalness of the forest,
with 0 (low) being monoculture plantation forests (which could be
native or non-native species) and 1 (high) being a natural species
and age-cohort mix without or with very little human intervention.
The assessment was done based on a review of the literature,
with additional information provided in Table S1.4 in the
supplemental information.

Bioenergy

Bioenergy crops are typically classified as either 1st or 2nd generation, with 2nd-generation lignocellulosic crops such as *Miscanthus* or short-rotation coppice. Second-generation bioenergy is considered to be much higher yielding with fewer input requirements,³⁹,⁴⁰ such that “low” at the x axis corresponds to all bioenergy being 1st-generation bioenergy (e.g., maize, but also wood collected from forests) and “high” corresponds to all bioenergy being provided as dedicated 2nd generation. The vertical dimension is related to the contribution of bioenergy to the world’s global primary energy production (which is presently ca. 640 EJ a⁻¹¹). Given that most scenarios that explore the impact of bioenergy on other sustainability considerations other than climate change mitigation do not assume more than a maximum of ca. 300 EJ to be provided by bioenergy, we capped the y axis at 320 EJ a⁻¹ (100%). The assessment was done based on a review of the literature, with additional information provided in Table S1.5 in the supplemental information.

EXPLORING SOLUTION OUTCOMES ACROSS THE
BIODIVERSITY-CLIMATE-FOOD NEXUS

Food production on land

Since the early 1960s, global cropland area has expanded by
nearly 7% (ca. 1.02 mio. km²) and production has more than
tripled to feed today's ca. 8 billion people.⁴,⁵ In the recent two
decades, the mean annual cropland expansion rate was
0.37% p.a., at a normalized production efficiency value of ca.
0.56 (Figure 2A, “c”; see supplemental information for further
information). The supply of sufficient and nutritious food to all re-
mains one of the foremost societal challenges.⁸ This goal could
in principle be achieved by further expanding agricultural land
(Figure 2A, “1”), but the conversion of natural land into managed
land together with agricultural intensification have severely
reduced local biodiversity such that already now more than
70% of humans live in an environment in which the “planetary
boundary” for relative species abundance has already been
transgressed.⁴² Given that many cropping systems are mono-
cultures with a large degree of mechanization on homogeneous
landscapes, further expansion of agricultural area is expected
to add additional stress on biodiversity.⁴³,⁴⁴ In addition, crop-
lands typically have lower carbon content in vegetation and soils
compared to the natural system they replace. Conversion of nat-
ural into agricultural systems causes annual CO₂ emissions of
c.a. 1.2 Gt a⁻¹⁴⁵ and has contributed 40% of the cumulative total
global CO₂ emissions since 1850.⁴⁵ Cropland expansion without
further biodiversity and carbon loss is considered unattainable⁴⁶
and not directly modulated by effectiveness (Figure 2A, “1” and
“2”). Our visualization does not capture indirect effects, as effec-
tiveness impacts the rate of necessary cropland expansion
required to produce more food—and the negative effects on
biodiversity and emissions from agricultural area expansion
could be reduced by increasing production efficiency to
compensate for the need for additional expansion. Likewise,
while restoration of natural ecosystems at the expense of
managed land is seen as an important aspect of biodiversity
conservation, with co-benefits for climate change mitigation,¹⁶
negative consequences for food supply (Figure 2A, “3”) could
only be avoided if enacted in parallel with other measures such
as reducing food wastage or dietary changes (see section “(Agricultural) food consumption” and Figure 2D). Expanding cropland in areas of high production efficiency, with sufficient but no over-supply of e.g., agrochemical or irrigation-water use, could, however, come with rapid gains regarding global yields and food supply.⁴⁷

Between 1985 and 2005, global yields rose by ca. 28%, of which ca. 20% were attributed to fertilizer application.⁴⁸ Increasing production intensity could be a much more promising route to reduce hunger and malnourishment than agricultural expansion if measures taken to do so would not interfere with other sustainability objectives in the biodiversity-food-climate nexus. Yet, at present, the world's food production system is far from such a trajectory. Inefficiencies in fertilizer application rates lead to over-fertilization, resulting in groundwater contamination, eutrophication of freshwater, and estuarine ecosystems, to the detriment of biodiversity within these ecosystems⁴⁹ (Figure 2B, “c”). The global mean fertilizer application rate is 150 kg/ha p.a. (weighted by crop area) or ca. 0.3 when normalized (see supplemental information). Pesticide use has increased by >80% over approximately the last three decades (https://www.fao.org/faostat/en/#home). Agriculture alone accounts for approximately 70% of global freshwater withdrawals—in some regions up to 95% (FAO 2017)—and is a major contributor to climate change. Aside from CO₂ emissions from agricultural expansion, management on existing croplands is a large source of N₂O and CH₄ emissions.⁵⁰,⁵¹ An unknown quantity is lost in addition as reactive N (such as NH₃ or NOₓ), with additional climate impact through their contribution to the formation of tropospheric ozone or secondary organic aerosol.⁵⁰ Overall, agricultural production is responsible for 13%–31% of annual anthropogenic greenhouse gas (GHG) emissions (2010–2019;²²), the food system's contribution as a whole (including transport and fertilizer production) amount to >25%⁵ (Figure 2B, “c”).

Increasing production efficiency by minimizing yield gaps is an important option to increase food supply without expanding croplands. At high efficiencies, fertilizer application rates of around 80–100 kg ha⁻¹ a⁻¹ globally (ca. 20% on y axis and assuming potash and phosphorus follow nitrogen fertilizer; Figure 2B, “1”), seems a realistic target to meet future food demands,⁴⁹ while substantially reducing impacts on biodiversity and GHG emissions. Over-fertilization is still prevalent in many regions (Zhang et al.⁴⁹; Figure S1), with rates above 200 kg/ha/year in western countries, which can result in high yields but with associated biodiversity and climate consequences. In practice, very high fertilization rates can also be harmful to crops and contribute to acidification of soils or long-term declines in soil organic matter.⁵²,⁵³ National data on toxic fertilizer application rates is lacking, but the FAOSTAT shows around the year 2000–2005, diminishing returns for yields, when application rates exceeded 300 kg/ha (Figure 2B, “3”). Moreover, a recent study⁵⁴ showed that some of the top crop-producing countries are still able to increase their yields despite lowering their fertilizer application rates, placing them among the countries with the highest fertilizer use efficiency worldwide (e.g., Denmark, France, Germany, and Austria; Figure 2B, “2”). This suggests that there is further room to lower fertilizer application rates while maintaining high outputs.

The size of the yield gap is partially related to the availability and efficient use of fertilizer but also reflects other factors such as irrigation, pesticides, technology, crop varieties, machinery, and knowledge.⁵⁵ These are to a large degree correlated. In order to close the yield gap, it is more important to apply inputs efficiently, rather than simply applying more. Thus, we imply for closing the yield gap an increase in intensity would not automatically have further benefits, while a variety of approaches to reduce overfertilization such as precision agriculture, adaptive farming practices, alternative farming systems (e.g., indoor farming), and crop breeding are powerful tools to reduce pressures on cropland expansion and thus reduce biodiversity loss and GHG emissions (Figure 2B, “1”).²⁸,⁵⁶,⁵⁷

## Fisheries

Seafood is an important source of nutrients, not only for coastal populations but also for people living inland supported by increased transport, preservation, and refrigeration capacities.⁵⁸⁻⁶⁰ Since the 1960s, the annual increase in seafood consumption (3.1% per year) has both exceeded that of human population growth (1.6% per year) and that of land-animal protein consumption (2.1% per year).⁶¹ Today, the per-capita annual seafood consumption is on average 20.5 kg, providing 17% of the world population's intake of animal proteins.⁵⁹ Wild fisheries have provided the majority of food from the sea (70% in 2020),⁵⁸ even though recent data show that global aquaculture production surpassed that of capture fisheries in 2022, representing 51% of the total aquatic animal production,⁶² in line with scenarios of mariculture.⁵⁹

The latest FAO global assessment shows that the proportion of overexploited marine stocks (37.7%) has reached a historical record,⁶² meaning that both marine biodiversity and fisheries' sustainability are threatened⁶³,⁶⁴ (Figure 2C, “c”). Under current management effectiveness (IUU fishing risk index is 2.28 at global scale³²), the transition between green and gray for fish biomass stands at about 75% of sustainably fished stocks, just below the current situation in the US (81% of sustainable fished stocks; NOAA⁶⁵; Figure 2C, “1”; see also Data S1 in the supplemental information). This is still well below the situation in the 1970s when the first collapses in major fish stocks happened with only 10% of the fish stocks being overexploited (FAO Fishstat, http://www.fao.org/fishery/statistics/en)—at a time when the rate of increase in global fishing effort began to take off sharply and the decreasing catches per unit effort reached an inflexion point in many parts of the world.⁶⁶,⁶⁷ In the Mediterranean Sea, for instance, the proportion of sustainably fished stocks is far below the global situation and very far from reaching the 75% transition level (Figure 2C, “2”). However, changes in management and fisheries regulation in the region have led to a very rapid shift toward more sustainable fishing, with the number of sustainably fished stocks jumping from 25% in 2018⁶⁸ to 42% in 2021.⁶⁹ A similar rate of change in the future would ensure the recovery of fish stocks in the region, which is currently one of the most overexploited in the world. It is worth noting that sustainable fishing can still negatively impact biodiversity: when stocks are exploited at FMSY, their biomass is reduced to ca. 0.35–0.4 of their pristine biomass.⁷⁰ So the maximum biomass represented in the green shoot as the greenest corner (100% sustainably fished stocks and no IUU fishing; Figure 2C, “3”) is still far from the pristine
<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 2.** Food production and consumption patterns</figcaption></figure>

(A) Impacts of cropland area change as indicator for conversion of natural into managed land; expressed as % change globally in 2021 since the year 2000. The red horizontal line highlights that in this case the vertical dimension changes from zero to positive (further cropland area expansion) and negative (illustrating cropland area decrease).

(B) Impacts of N-fertilizer application on cropland as indicator for intensification of production, normalized by the global mean of national fertilizer application rates.

For (A) and (B), national yield gap estimates are used as a measure of effectiveness plotted such that a value of 1 (high) corresponds to the case of zero yield gap (i. e., (1-yield gap)).

(C) Impacts of the proportion of sustainably exploited wild marine populations as an indicator for fishing pressure (y axis) and of the proportion of illegal, unreported, and unregulated fisheries as an indicator for management inefficiency (x axis). Impacts on fish biomass, as an important component of biodiversity and fishery yield, which is an important food source. Changes in sustainably exploited stocks of fish do not have a direct climate-change mitigation impact; therefore, this panel is missing in (C).

(D) Impacts of changes in consumer food and feed demand, indicated by animal protein in diet. Each metric was calculated across a two-dimensional surface of percentage of plant-based foods in the diet on the y axis (in terms of mass of protein intake) and a metric of consumer efficiency on the x axis, based on country-level information. Dietary changes do not directly impact food availability; therefore, this panel is missing in (D).

All panels: Labeling and color scale as in Figure 1. Further information: methods section, supplemental information, and Tables S1.1–S1.3.

biomass of fish. On the other hand, when targeting the maximum economic yield (MEY, which is sometimes adopted as the management target rather than the MSY, e.g., in Australia;⁷¹), the

fish biomass is reduced less, to ca. 0.5–0.6 of their pristine biomass.⁷⁰ The biodiversity status could improve when IUU fishing decreases (management effectiveness increases; Figure 2C,
“4”). Costello et al.⁷² estimate that applying $F_{MSY}$ to all stocks of conservation concern would increase fish biomass at 994.4 million tons (current biomass is estimated at 840.3 million tons in early 2010s). This represents an increase of 18.34%. Our calculations suggest that transitioning to all stocks sustainably fished and halting IUU fishing could potentially increase fish biomass by close to 37% (corresponding to maximum effectiveness and 100% stocks sustainably fished; Figure 2C, “3”).

Regarding fishery yields, and hence the contribution to global food supply, under current management effectiveness (Figure 2C, “c”) the transition between green and gray corresponds to the situation in the late 1980s (in 1987, total catches reached 78 million tons with 75.7% stocks sustainably fished [https://www.fao.org/fishery/en/resources]) where catches started to level off. On either side of this transition, we find the current situation of the United States with 81% of sustainably exploited stocks⁶⁵ Data S1 supplemental information and the global fisheries situation in 2021 with 62% of the exploited stocks within biologically sustainable levels (Figure 2C, “1”; see also Data S1 in the supplemental information).⁶² IUU contributes to overexploitation.³² Halting IUU (Figure 2C, “4”) would allow to sustainably exploit more stocks and to increase yield. Costello et al.⁷² show that reaching $F_{MSY}$ would increase catches by 17% (this consolidates an earlier study by Ye et al.⁷³ estimating a yield increase of 16.5%), compared to the current situation. Our calculations estimate that we could then expect 36% more catch if 100% stocks were sustainably exploited and IUU halted (Figure 2C, “3”).

Depending on the scenario, the catch for marine seafood may increase by between 36% and 74% by 2050 compared to current yields.⁵⁹ Target 6 of the CBD Strategic Plan for Biodiversity, the FAO Code of Conduct for Responsible Fisheries, and SDG 14.4 urge nations to stop overexploiting marine populations and put in place rebuilding strategies (100% sustainably exploited stocks). Such a strategy combined with halting IUU fishing would allow a substantial increase in both fisheries production and fish biomass in the sea (one estimate would be ca. 36%); this is particularly critical in the context of climate change with projections of up to ca. 20% decrease in fish biomass under high warming scenarios by the end of the century.⁷⁴

## (Agricultural) food consumption

Animal products, and especially ruminant meat, are more resource intensive to produce than plant-based foods, requiring more land and water, and are associated with higher rates of greenhouse gas (GHG) emissions.⁷⁵ Today's crop and pasture area together are approximately 50% of the ice-free land area, around 50% of this global agricultural area is used for feed production or grazing.⁷⁶ Approximately 25%-30% (around 7 million km²) of the area used for grazing would also be suitable for growing food crops.⁷⁷ Country statistics rebased to the 0%-100% scale (see supplemental information) indicate that as a population weighted average globally (2021) 41.2% of the protein consumed is from plant-based foods (Figure 2D, “c”). This is notably lower than a diet recommended for healthy and sustainable consumption.³⁴ In addition to the too high and inequitable animal-protein consumption, a third (24%-40%) of the food commodities produced globally are lost or wasted on the way from the field to the consumer.³³,⁷⁸,⁷⁹ Likewise,

consuming food in excess of nutritional requirements (i.e., over-eating) can be considered as a loss to the food system.⁸⁰ Using country statistics and rebasing to the 0–1 scale, the present-day (Figure 2D, “c”) country-average efficiency was 22.3%, after also accounting for undernourishment (see supplemental information). Food waste and losses are thus another important factor when assessing consumption impacts in the food, climate, and biodiversity nexus.

An agricultural area of 25% of the ice-free land has been put forward as an area under use for which global biodiversity would not be affected negatively overall,⁴⁴,⁸¹ which is approximately the area needed to feed the world's population on a diet similar to the average food consumption of India.⁸² Reduction in animal-protein overconsumption (mostly the western world) has been identified in numerous studies as a key factor in stopping biodiversity loss from further conversion of natural lands and making room for protected area for biodiversity conservation (Figure 2D, “1”).¹⁸,⁸³⁻⁸⁷ This includes, for instance, reducing the export of beef and of soy used as feed from Latin America and SE Asia predominantly into Europe, China, Russia, and the Middle East, which are a major driver of loss of tropical rainforests and savannahs.⁸⁸

Halving animal-product intake through changes in animal-rich “Western diets” in combination with avoiding meat from producers with above-median GHG emissions was estimated to make available 21 million km² of agricultural land and reduce GHG emissions by nearly 5 GtCO₂-eq a⁻¹.⁷⁵ These estimates increase to up to 10 GtCO₂-eq a⁻¹, around a quarter of today's anthropogenic greenhouse gas emissions,⁸⁹ when vegetation carbon uptake is considered on converted agricultural land.⁷⁵ Here (following Alexander et al.⁸²; see supplemental information) we calculated agricultural area to require only 7% of the ice-free land in a hypothetical scenario corresponding to a global vegan diet (and 100% consumer efficiency), which could make large areas available for ecosystem restoration and also other climate change mitigation measures (see, e.g., sections “afforestation/reforestation” and “bioenergy,” below). A dietary shift toward less meat would also have large co-benefits for climate change mitigation¹⁰,⁹⁰ (Figure 2D, “1” and “2”).

Animal grazing can, however, also benefit biodiversity locally by maintaining open landscapes. Especially at relatively low stocking density, grazed pastures and silvopastoral systems often are species rich⁷⁶,⁹¹ and store substantial amounts of carbon below ground.⁹²,⁹³ Overall, studies show diverse impacts of low grazing intensity on biodiversity, depending on the climatic environment, grazing species, and biodiversity indicator assessed.⁹⁴⁻⁹⁶ In our assessment, we do not assume a lessening of the positive impact on biodiversity in a completely vegan world (Figure 2D, “2”) but note that this assumption comes with very high uncertainty and may be too optimistic. Likewise, a notable change in global animal protein consumption could have regionally diverse spill-over effects via food and land prices, declining share of agriculture in countries' GDP, changes in food commodity trade, and economic consequences for non-food sectors that could reduce some of the positive outcomes, including for biodiversity and greenhouse gas emissions.⁹⁷ While a shift toward plant-based diets has substantial benefits for biodiversity and climate, the reduction in pressure on land is more rapid, as
livestock production today is a higher percentage of agricultural land in comparison with the proportion of GHG emissions from the agricultural system. This gives rise, for example, to the differences in the slopes of lines of equal score between Figure 2D, “1” and “2.”

More than 20% of the global crop area is used to produce food that is not eaten,⁷⁹ an area indicative for the detrimental impacts food losses and wastage have on biodiversity and the large GHG emissions associated with them. On-farm losses alone have been estimated as 16% of global agricultural-related GHG emissions.⁹⁸ Others state that if total GHG emissions from food waste—without accounting for emissions from land-use change—were attributed to one country, it would be the third largest emitter globally.⁹⁹ A recent analysis attributed nearly 50% of the total food system’s GHG emissions to food loss and waste.¹⁰⁰ Food system losses from over-consumption of food may be of a similar magnitude as the losses from food thrown away by consumers.³³ Reducing the losses of foods by retailers and consumers (including over-consumption) jointly with a more healthy and equitable distribution of animal protein intake has the potential to provide a substantial reduction in the demands placed on agriculture and hence improve global sustainability (Figure 2D, “2”).

Despite increasing public and media coverage on the health and environmental benefits of low-meat diets, per-capita meat consumption in countries such as the USA and Europe are relatively static and globally continue to rise, with higher incomes linked to greater total calorie and protein consumption and diets containing more animal products.¹⁰¹⁻¹⁰³ For meat over the coming decade, a global increase of 2% has been projected, with strong trends in middle-income countries.¹⁰³ Shifts toward consumption of poultry might reduce pressures on land to some degree, but projections also expect dairy production to be the fastest growing agricultural commodity.¹⁰³,¹⁰⁴ Continuation of these trends toward the global population consuming a diet similar to today’s average “Western” diets (e.g., supplemental information, Figure S3) would continue to drive land-use change and agricultural intensification with high agrochemical use, and hence GHG emissions and biodiversity loss, especially if such a trend would also be accompanied by high levels of food waste and over-consumption (Figure 2D, “3” and “4”).

## Afforestation/reforestation

Forests cover a remaining 40 (32–43) mio. km² of an estimated potential maximum natural cover of ca. 50–55 mio. km² (Figure 3A, “c”).²⁷,³⁸ Avoiding further forest deforestation is critical for climate regulation and for biodiversity.¹⁰⁵⁻¹⁰⁷ Recent decades have seen the expansion of forest area in temperate regions while tropical deforestation continues.²⁷,¹⁰⁸ The global vegetation carbon pool in forest biomass on today’s forested land has been estimated as 68%–80% of its (non-harvested) maximum,¹⁰⁹ less than 40% of forest area contains forests older than 140 years¹⁰⁸ (Figure 3A, “c”). Given that forests provide habitat to a wide range of species, deforestation is a well-established cause of biodiversity loss (Figure 3A, “1”). Forest degradation (as a result of, e.g., replacing mixed-species forests by even-aged monocultures, selective logging, removal of dead wood) is an additional major driver, which is much less well documented (Figure 3A, “2”).¹¹⁰,¹¹¹ Likewise, carbon losses

associated with deforestation are large, additional losses from degradation (in regrowing but also in mature forests) are—as for biodiversity—under-studied, but potentially also considerable.¹¹²,¹¹³ These effects are amplified by tree mortality in the wake of extreme weather events and their interactions with insects and fire.¹¹⁴,¹¹⁵

Restoring natural forests beyond today’s areal coverage is expected to have a positive impact on biodiversity, in particular if natural regrowth is facilitated or when a mix of native tree species is planted (Figure 3A, “3”).¹¹⁶⁻¹¹⁹ But afforestation in principle can also take place in regions that naturally would have little woody cover, such as savannas or drained wetlands, which would destroy the local biodiversity in these ecosystems (Figure 3A, “4”).¹²⁰⁻¹²² In addition, monoculture plantation forests have considerably lower biodiversity value than primary or secondary forests.⁴⁴ They can even be detrimental to biodiversity if a planted species becomes invasive.¹²³

From a carbon cycle perspective, and even without considering area expansion, young forests typically have notable carbon uptake. Forests regrowing after clear-felling have been found to contribute around one-third of the annual forest carbon sink in 2001–2010,¹⁰⁸ while others studies imply that the sink in regrowing forests may be even larger.¹⁰⁸,¹²⁴,¹²⁵ Afforestation and reforestation are often considered cost-effective climate change mitigation options.¹²⁶ Projected future carbon uptake rates in some scenarios reach up to an additional annual uptake of around 3 Gt a⁻¹, a number would double today’s entire global carbon sink on land. However, these projections are contingent on assumptions about large-scale forest area expansion¹²⁶,¹²⁷ and do not consider the broader ecological roles of forest, risks to forests from climate change, or socio-economic constraints that could range from land ownership or availability of tree seedlings to short-comings in carbon markets.¹¹⁵,¹²⁸,¹²⁹ By contrast to highly diverging impacts on biodiversity from natural vs. non-natural forests area expansion, differences in net carbon uptake and storage are smaller,¹³⁰ but intensive forest management or forests expanding into other carbon-rich ecosystems are much less efficient (or even negative) from a carbon cycle perspective compared to naturally regenerating forests (Figure 3A, “3” and “4”).¹¹⁹,¹³¹ Monoculture tree plantations are also particularly vulnerable to storms, fire, and pest outbreaks.¹¹⁹ Grasslands store approximately 20% of the world’s soil carbon, and contribute globally >20% to the total land carbon sink,¹⁰⁹,¹³² so replacement by forests carries large risks of severely misjudging the intended benefit for climate change mitigation. Biophysical surface exchange processes can lead to local cooling or warming, while forests’ biogenic volatile organic carbon emissions interact with atmospheric chemistry in complex ways, which adds large uncertainty to forest-related climate change mitigation expectations.⁵⁰,¹³³,¹³⁴

One of the most contentious issues of expanding forest area (whether for climate change mitigation or restoration or both) is related to competition with land used for food production. Having too little forests would likely have negative implications even though this would potentially provide more land for agriculture, buffering increasing calorific demands of a growing human population. But forests also provide habitat for pollinators¹³⁵,¹³⁶ such that further loss of forest area would eventually be expected to affect food production negatively (Figure 3A, “1”)
<figure><img src="image_7.png" /><figcaption>**Figure 3.** Forest area and bioenergy supply</figcaption></figure>

<figure><img src="image_8.png" /></figure>

(A) Impacts of climate-change mitigation through forest area expansion (expressed as % of potential forest cover); the degree of naturalness of forest composition is used to indicate effectiveness. Some scenarios in the published literature assume extending forest area beyond its estimated potential (i.e., afforesting savannahs). We do not include such a possibility in the graph.

(B) Impacts of climate-change mitigation through planting bioenergy crops. Impact is assessed here in relation to land carbon uptake and storage. Bioenergy is related to today's primary energy production. In most scenarios in the literature, the maximum bioenergy in the total energy mix explored is 300 EJ, which is nearly ca. 50% of today's global total. 100% therefore is equivalent to 320 EJ (see methods). Effectiveness is 1st- vs. 2nd-generation bioenergy. Labeling and color scale as in Figure 1. Further information: supplemental information and Tables S1.4 and S1.5.

and “2”). In practice, a world without forests would be uninhab-
itable due to climate feedbacks. Forest area expansion at large
scales will, however, compete with land for agricultural produc-
tion, with little difference arising from the forest type (Figure 3A,
“3” and “4”).¹³⁷,¹³⁸ Such competition for land would reduce
crop production globally, unless compensated by further
management intensification on existing agricultural land to
enhance yields¹⁰,¹²⁶—with associated GHG emissions—as
well as increasing food prices.¹³⁸

Bioenergy

Increasing reliance on bioenergy, with carbon capture and stor-
age, is a fundamental characteristic of GHG emission trajec-
tories that require CO₂ removal from the atmosphere to limit
global warming to 2°C or below. ¹⁰,¹²⁶,¹³⁹ At present, bioenergy
provides around 45 EJ a⁻¹,⁴¹ less than 10% of the total global
energy demand of ca. 640 EJ a⁻¹ (rescaled to 14% on our y
axis). Only around 3–5 EJ a⁻¹ is from dedicated energy
crops⁴¹,¹⁴⁰ (Figure 3B, “c”). Most of the global bioenergy is still
used in the form of traditional wood-fuel, of which up to one-third
is harvested unsustainably and also combusted ineffi-
ciently. ⁴¹,¹⁰⁶,¹⁴⁰ The current situation is in stark contrast with
some projections of future bioenergy supply that increase to
well over 150 EJ a⁻¹ in only a few decades. ¹²⁶ In some projec-
tions, the necessary land area required to grow bioenergy crops
is equivalent to up to 50% of today’s cropland, which is why
these projections are regarded as unsustainable from many
environmental and societal perspectives. ¹⁰,¹²⁶,¹²⁸,¹⁴¹,¹⁴²

For strong climate change mitigation scenarios, the required
expansion of land area for bioenergy crops was found to have
similarly negative impacts on terrestrial biodiversity as with
unmitigated climate change,¹⁴³,¹⁴⁴ even for 2^nd^-generation
bioenergy, due to a reduction in species’ ranges and pressure
on protected areas (Figure 3B, “1” and “2”). Agricultural intensi-
fication can reduce competition for land but is likely to be
accompanied by substantial increases in agricultural water
withdrawals, fertilizer use, organic soil C losses, nitrate leaching
and N₂O emissions, with further ramifications for biodiversity and
climate change (see, e.g., Figure 2B).

Lignocellulosic crops have generally larger yields than 1st-gener-
ation bioenergy crops<sup>39</sup> while requiring less fertilizer or irriga-
tion inputs.<sup>40</sup> Moreover, perennial crops can also increase local
habitat diversity,<sup>145,146</sup> which is why replacement of today's still
mostly 1st-generation bioenergy by similar energetic returns of
2nd-generation bioenergy is considered a relative gain—
although reducing the area used for bioenergy crops will overall
be beneficial for biodiversity (<u>Figure 3B</u>, “3” and “4”).

Policies that promote large-scale use of biomass for energy
production are problematic since they tend to assume that
biomass-based energy is per se carbon neutral. ¹⁴⁷ In practice,
the impact of bioenergy cropland expansion on carbon storage
in ecosystems is highly uncertain. First-generation bioenergy
requires substantial energy input to convert the harvested
biomass into useable fuel, which greatly reduces its area-effec-
tiveness¹²⁶ and contributes to large carbon losses resulting from
land being converted into energy crops (or from indirect land-use
changes associated with bioenergy cropland expansion; Figure 3B, “1”).<sup>147–149</sup> But even for 2<sup>nd</sup>-generation bioenergy crops, a number of process-based ecosystem models have found that cumulative net carbon uptake rates over time were considerably lower, compared to bioenergy carbon uptake numbers calculated in integrated assessment models.<sup>150–152</sup> While the reasons for this discrepancy are not yet fully resolved, these studies raise doubt about how realistic some of the postulated mitigation potentials from bioenergy (together with carbon capture and storage) are in practice, given large area requirements also in 2<sup>nd</sup>-generation scenarios and when full ecosystem carbon cycling and legacy effects of land conversions are considered (Figure 3B, “2”). Still, at moderate levels, 2<sup>nd</sup>-generation bioenergy crops such as Miscanthus or woody short-rotation coppice have substantially lower impact compared with 1<sup>st</sup> generation bioenergy because of reduced fertilizer needs and a larger proportion of the aboveground biomass being used for energy production.<sup>145,153</sup> In field studies these crops have also been found to contribute to ecosystem carbon restoration and/or enhance habitat diversity compared to current bioenergy crops (Figure 3B, “3”).<sup>154</sup> This is why complete absence of 2<sup>nd</sup>-generation bioenergy (if grown on cropland) could be even less favorable for ecosystem carbon balances (Figure 3B, “4”) compared, for example, with a complete absence of 1<sup>st</sup>-generation bioenergy.<sup>106,145,153,154</sup>

Given that today cropland and intensive permanent pastures already cover nearly 20 mio. km² and extensive pastures on potential forested sites take up an additional ca. 9 mio. km²,²⁷ the further conversion of substantial natural land area for growth of bioenergy crops must be regarded critical also from a food perspective. Attribution of bioenergy impacts on food prices is challenging and depends on the methodology applied, regions and period investigated, and type of bioenergy crop.¹⁴⁰,¹⁵⁵ However, already for the first decade of the 21st century, the enhanced use of food crops for the production of bioenergy (e.g., from grain, oilseeds, or vegetable oil) has been estimated to contribute significantly (e.g., order 15%-30%) to observed food price increases, such as for US corn.¹⁴⁰ In simulations that include future expansion of bioenergy crops, food (and water) prices have been shown to increase notably, caused by multiple factors, such as carbon prices, enhanced costs of intensification (in studies that seek to restrict cropland expansion) or competition for land<sup>127,156,157</sup> (Figure 3B, “1” and “2”). Second-generation bioenergy should in principle have lower impacts because of higher yields and because lignocellulosic crops do not directly compete with food production. Nevertheless, enhanced use of non-food bioenergy crops will still compete for cropland area and hence affect food prices (Figure 3B, “2” and “3”).<sup>126,140</sup> Both the price increases and impacts on human societies depend strongly on the underlying socioeconomic assumptions. Hasagewa et al.<sup>158</sup> pointed out that mitigation policies across a number of scenarios resulted in increases in food prices and risk of hunger compared to scenarios without carbon prices, even exceeding climate change impacts on food prices.

Overall, by applying EU renewable energy sustainability criteria globally, a 2<sup>nd</sup>-generation bioenergy potential of 88 EJ a<sup>-1</sup> (28% on our x axis) has been estimated, with the authors cautioning that this may well be reduced to annually ca. 50 EJ a<sup>-1</sup> depending on uncertainties related to future crop-yield

growth.<sup>159</sup> Others have put forward around 60 EJ a<sup>-1</sup> as a conservative estimate based on studies that restrict bioenergy crops to “marginal” land and exclude expansion into currently protected areas.<sup>126</sup> Relying on large amounts of bioenergy, which still underpins many climate change mitigation scenarios, will cause environmental and societal harm and interfere with the achievement of several other sustainable development objectives.<sup>10,126,141,160</sup>

TOWARD SUSTAINABILITY IN LAND AND MARINE
SYSTEMS

Clearly, the loss of biodiversity, climate change, and feeding a
growing population are three critical and tightly interlinked chal-
lenges. While the magnitude of the problem at hand is well under-
stood, the scientific community is increasingly tasked with identi-
fying solutions to support international policies.<sup>161-164</sup> The Green
Shoots examples presented here bring to light a number of impor-
tant opportunities in avoiding dangerous levels of anthropogenic
interference in both the climate and socio-ecological systems.
Our visual assessment immediately highlights that we are currently
(as indicated by the placement of the “c”) on the edge of (or below)
“sustainability” across most of these indicators, hence the choices
made now will have a significant impact on nature and societies in
the future; these choices can tip the balance either positively or
negatively. Many options to intervene exist that could result in pos-
itive outcomes compared to the present day, and some of these
could even already be within reach with relatively little additional
effort. For instance, reducing fertilizer input (conjointly with other
agrochemicals, for which fertilizer is used here as a surrogate)
can lead to rapid improvements in biodiversity and climate change
without necessarily being detrimental to food production. Never-
theless, the indicators for which data are available for individual
countries (Figures S1-S3) provide a reminder of the large discrep-
ancy that exists between countries regarding their “distance” from
more sustainable levels on the Green Shoots’ surface. There is no
reason to believe that this picture would be different for other indi-
cators analyzed, for which we cannot underpin our literature-
based assessment with national statistics. Given that the causes
of biodiversity loss, climate change, and food insecurity often lie
outside of a country’s boundaries, and the existing between-coun-
try economic inequality, a globally coordinated effort toward sus-
tainability is expected to be much more substantive than uncoor-
dinated national-level interventions.

Due to the large number and variety of solutions discussed in
this paper, it is not possible to make a quantitative comparison
across different Green Shoots. Nevertheless, side-by-side they
provide qualitative evidence of the potential strength of reducing
human impacts when different actions are combined, with
multiple co-benefits arising for biodiversity, climate change, or
food provisioning. Co-benefits were identified already between
both extending and improving effectiveness of the protected
areas network, and provisioning and regulating ecosystem
services. ¹⁸ Likewise, positive synergies are obvious between
adjusting diets toward an overall healthy and equitable animal pro-
tein intake, reducing food waste, and measures to reduce expan-
sion and/or over-intensification in agriculture and unsustainable
fisheries (Figure 2C). Large challenges can be expected, however,
in achieving these synergies. SDG 12 (responsible production and
consumption), for instance, has been identified as the SDG with the highest degree of trade-offs when pairs of SDGs were compared—in this case, with e.g., SDG 10 (reduced inequalities), SDG 1 (no poverty), SDG 6 (clean water and sanitation), and SDG 3 (good health and well-being).¹⁶³ Others have found that substantive efforts related to carbon prices and climate financing and access to nutritious food and clean energy are needed to enhance synergies between SDGs.¹⁶⁴

As things stand, however, many scenarios used in the climate change or biodiversity literature expect future pressure on land and sea use to increase substantially as demand for food increases to feed a growing population with higher per-capita consumption of calories and animal protein, and large land areas dedicated to reforestation/afforestation or bioenergy to combat global warming.<sup>10,82,126</sup> It may be possible to meet these additional demands for land area, but these are accompanied by a high risk of increasing food prices and regional inequity in food supply.<sup>165,166</sup> Still, ambitious future targets in climate change or biodiversity policies are attainable, conditional on our success in increasing agricultural productivity in an environmentally favorable manner and in achieving a globally more equitable diet, especially for the consumption of animal protein.<sup>165,166</sup> These results support the growing consensus of enhanced integration of CBD and UNFCCC targets and related policies with the SDGs.

Critically, it is not only the intervention *per se*, but the effectiveness of its implementation that is fundamental for success. With very few exceptions, this is seen across all of the Green Shoots. While individual studies have previously highlighted the importance of implementing response options “well” (including e.g., the effectiveness of fertilizer use,<sup>167</sup> the “carbon effectiveness” of land use,<sup>168</sup> or effectiveness of protected areas<sup>18,169</sup>) we demonstrate this here across the biodiversity-food-climate nexus. Consequently, a combination of multiple levers is available toward achieving sustainability objectives. Adjusting the analytical framework that underpins the Green Shoots based on regional information provides the opportunity for individual nations to identify their placement on the colored surface. Countries could then decide whether the largest gains for climate, biodiversity, and people can be obtained both regionally and globally by increasing an intervention itself, or both. This will support the adjustment of existing policies and associated investments.

Given the current international policy dynamics such as the targets under the CBD's post-2020 KMGB framework, measures put in place to achieve climate change mitigation and adaptation under the UNFCCC it seems important for the scientific community to support these processes by evaluating and communicating existing opportunities to act. The Green Shoots intend to complement the risk assessment of the Burning Embers by highlighting the solutions that exist to reduce risks—to the environment and consequently also to people—including the potentially large cumulative and synergistic effect of multiple solutions applied simultaneously.

### ACKNOWLEDGMENTS

The authors acknowledge the discussions and suggestions in an earlier draft of the manuscript received by Dr. Joel Smith. Leonard Pfaff helped with drawing *Figures 1, 2, and 3*. A.A., M.R., and R.F. acknowledge financial support by the Helmholtz Association. J.C. received financial support from BiodivERsA

(MOVE), Fondation de France (MultiNet), and the EU (MARHAB; GES4SEAS). P.A. acknowledges support by the UKRI (BB/W018152/1; 10075849; 10039590). M.C. acknowledges financial support from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 869300 (FutureMares project) and no. 101059877 (Ges4Seas project) and the institutional support of the "Severo Ochoa Centre of Excellence" accreditation (CEX2019-000928-S). P.L. acknowledges support from the LabEx BASC (ANR-11-LABX-0034). Y.-J.S. acknowledges financial support from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 869300 (FutureMares project) and from the 2017-2018 Belmont Forum and BiodivERsA joint call under the BiodivScen ERA-Net COFUND program (Sombee project) and with the funding organization ANR (ANR-18-EBI4-0003-01).

### AUTHOR CONTRIBUTIONS

A.A., P.L., and Y.-J.S. had the original idea for the Green Shoots visualization, which was subsequently elaborated further by all authors. A.A. wrote the first draft and led the assessment that underpins *Figures 3A and 3B*; P.A. led *Figure 2D*, R.F. *Figures 2A and 2B*, and Y.-J.S. *Figure 2B*. All authors contributed to the writing of the paper and finalizing the figures.

### DECLARATION OF INTERESTS

The authors declare no competing interests.

### SUPPLEMENTAL INFORMATION

Supplemental information can be found online at https://doi.org/10.1016/j.onear.2025.101455.

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Supplemental information

The biodiversity-climate-food nexus:
Illustrating challenges and solutions
using the Green Shoots framework

Almut Arneth, Peter Alexander, Richard Fuchs, Yunne-Jai Shin, Joachim Claudet, Marta Coll, Paul Leadley, Carlo Rondinini, and Mark Rounsevell
# The biodiversity-climate-food nexus: illustrating challenges and solutions using the Green Shoots framework

Almut Arneth¹,⁸, Peter Alexander², Richard Fuchs¹, Yunne-Jai Shin³, Joachim Claudet⁴, Marta Coll⁵, Paul Leadley⁶, Carlo Rondinini⁷, Mark Rounsevell¹,²,⁸

1. KIT, Dept. Atmospheric Environmental Research, 82467 Garmisch-Partenkirchen

2. School of Geosciences, University of Edinburgh, Drummond Street, Edinburgh EH8 9XP, United Kingdom

3. Institut de Recherche pour le Développement (IRD), Univ Montpellier, IFREMER, CNRS, MARBEC, Place E. Bataillon, CC 093, 34095 Montpellier cedex 5, France

4. ESE Laboratory, Université Paris-Saclay / CNRS / AgroParisTech, 91400 Orsay, France

5. Institute of Marine Science (ICM-CSIC), Passeig Maritim de la Barceloneta, Barcelona, 08003, Spain

6. National Center for Scientific Research, PSL Université Paris, CRIOBE, CNRS-EPHE-UPVD, Maison de l'Océan, 195 rue Saint-Jacques 75005 Paris, France

7. Global Mammal Assessment program, Department of Biology and Biotechnologies, Sapienza University of Rome, 00151 Rome, Italy

8. KIT, Dept. Geography and Geoecology, Karlsruhe

## Supplementary material

The Supplementary material includes the following information

1. Additional information on the methods used.

2. Additional information extracted from the literature, as summarised in Tables S1–S5

3. Supplementary Excel graphs, see Shoots_Nexus.xlsx: Contains individual worksheets that were used for the initial sketches of Figure 2 & 3. These worksheets contain the templates used for the Green Shoots panels in the main manuscript, and chosen colour values and transitions; these could be either associated with a linear or non-linear x-axis. Colour values range from -100 (grey) to 100 (green). The draft colour surfaces were subsequently smoothed using the krige function in the gstat (v2.1.1) R package and the autoKriging function in the automap (v1.1.9) R package (see also methods in ¹) and finally polished by a graphics designer. The worksheets could also be used by interested readers to explore additional measures.

The Green Shoots diagrams can be constructed wither based on a literature review and assessment, or from data, or both. In this paper we have chosen to demonstrate both approaches as described in the Methods section of the main manuscript and in the following text.

### 1.1 Food production on land (see also Supplementary Table S1)

We used the sum of application rates from nitrogen, potash and phosphorus from FAOSTAT for 2017-2021 (http://www.fao.org/faostat/en/#home; NPK) to assess production intensification. FAOSTAT provides no distinction on the use of fertilizers, however. Some countries fertilize their pastures (e.g. New Zealand), some their forests (e.g. Finland), and other countries rely heavily on indoor farming which generates a multitude of harvest cycles per year (e.g. The Netherlands), compared to conventional farming, which causes high fertilization rates when accounting all this for cropland in our analysis. In order to eliminate these obvious outliers, we considered only the 90th percentile for colouring our plots, with the 90th percentile and above being dark grey.
After exclusion of outliers, the country specific fertilizer application rates were normalized (see S2), which built the basis for the colour coding of the Green Shoots. The global mean fertiliser application rate is 150kg/ha p.a. of NPK (weighted by crop area) and represents todays status quo in the Green Shoots. This mean fertilizer application rate represents a normalized value of ca. 0.3 on the y-axis in Fig. S1. Nitrogen surplus ²,³ can be used as a proxy to assess the effectiveness of intensification regimes across the globe. For example, countries and regions such as China, India, US and Canada, Europe, or Brazil are those reported in ²,³ to have the estimated largest N-surplus (fertiliser applied above of what is needed by the plants).

Cropland area expansion was quantified as national 22-year averages in cropland area change as a percentage of the total national area of a country. We took data for the period 2000-2021 from FAOSTAT (http://www.fao.org/faostat/en/#home). The global mean cropland area expansion over that period was 0.37% p.a. (weighted by crop area), but with large variation between countries. Similar to the normalization of productivity intensification, we normalized cropland expansion between the minimum and maximum values of all countries (i.e. the country with maximum expansion having a normalized value of 1) to improve comparability across countries and between area expansion and intensification. For reference, the mean annual cropland expansion rate would represent a normalized value of ca. 0.56 on the y-axis in Fig. S2, while 0% expansion has a normalized value 0.5 (see red line in S3). This is redrawn in the Green Shoots diagram as deviation from the zero line for an easier visualisation of agricultural land expansion and -shrinkage. Using the 0.5 value in S3 as the reference point, normalized values above the 0.5 (expansion) are indicated in the Green Shoots with positive y-axis values whereas normalized values below 0.5 (shrinkage) are indicated with negative y-axis values (see Shoots_Nexus.xls).

For both figures (S2 & S3) national production efficiencies are shown on the x-axis, defined as attainable yield (=1) minus the yield gap. National yield gap estimates were derived from the national spatial mean of the gridded yield gap data from Earthstat.org ⁴,⁵. Yield gaps here are defined as the difference between the actual and attainable yields in a region (1-yield gap ⁵). The global mean yield-gap between 2000 and 2021 was 26,6% (weighted by crop area) referring to a normalized production efficiency value of ca. 0.56. We used the two data-driven diagrams with additional literature on impacts regarding biodiversity, GHG emissions and food productions to colour the Green Shoots (Table S1).

<figure><img src="image_10.png" /><figcaption>Figure S1: Production intensity using normalized fertilizer application rate as an indicator (y-axis; 90th percentile) vs. yield gap (x-axis; plotted such that an efficiency of 1 corresponds to the case of zero yield gap).</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>Figure S2: Production efficiency using normalized crop expansion rate as an indicator (y-axis) vs. yield gap (x-axis; plotted such that an efficiency of 1 corresponds to the case of zero yield gap).</figcaption></figure>

**1.2 Sustainable fisheries production** (see also Supplementary Table S2)

Fisheries production and its impact on fish biomass were analysed across a two-dimensional surface. The y-axis represents the proportion of sustainably exploited stocks, a common indicator (e.g., ⁶) which is calculated to track whether “the impacts of fisheries on stocks, species and ecosystems are within safe ecological limits” (Aichi Target 6 of the CBD) and fish stocks are restored “at least to levels that can produce maximum sustainable yield” (SDG 14.4). A stock is considered sustainably fished when fishing mortality F is kept below FMSY, a level that produces the Maximum Sustainable Yield (MSY). The FAO ⁷ estimated that globally 62.3% of fish stocks are exploited within sustainable levels in 2021, a proportion that has gradually decreased over time (reported as “c” in Figure 2C). Regional examples are provided with the US and the Mediterranean Sea (see also Supplementary Table S2). The y-axis is scaled between 0 (none of the fish stocks are sustainably exploited) to 1 (all stocks are sustainably exploited).

The x-axis provides a measure of the effectiveness of fisheries management. We chose to reflect the effectiveness in combatting Illegal, Unreported and Unregulated (IUU) fisheries, as the high proportion of IUU fishing is a main driver of overexploitation and stands in the way of efforts to rebuild depleted stocks ⁸. Preventing, deterring and eliminating IUU fishing is the main objective of the FAO Agreement on Port State Measures, and a target of the UN SDG 14.4. A frequently cited study ⁸ indicates that IUU catches worldwide would range between 11 and 26 million tonnes annually, which would represent up to a third of the total catch. These figures represent a conservative estimate, as the 'unregulated' dimension of IUU fishing was not considered ⁸. This estimate is the only one existing at global scale, and no recent update has been made. As a response, Macfadyen and Hosch ⁹ have developed the composite IUU Fishing Risk Index, to rank countries' performance in combatting IUU. We used the most recent update of the index in 2023. The IUU Fishing Index ranges from a score of 1 (good/strong) to 5 (bad/weak). In 2023, the world overall score was 2.28. The best-performing country is Romania (1.62) while the worse-performing country is China (3.69). The score for the Mediterranean sea is 2.23 while the US stands at 2.32
⁹ (https://iuufishingindex.net/). We standardized the IUU Fishing Risk Index for the effectiveness x-axis that is scaled between 0 (minimum effectiveness, corresponding to the upper estimate of IUU fishing risk index and 1 (lower estimate of IUU fishing risk index) ⁹.

For the biomass indicator (Figure 2C, left shoot), we considered the gains provided by an increase in management effectiveness ( combatting IUU fishing). Costello et al. ¹⁰ estimated that applying FMSY (the fishing rate that ensures the Maximum Sustainable Yield MSY) to all stocks of conservation concern would increase fish biomass to 994.4 million tons (current biomass is estimated at 840.3 million tons in early 2010s). So this represents an increase of fish biomass of 18.34%. If we consider that IUU fishing concerns overexploited stocks (IUU ~31% of global catch as an upper estimate; ⁸, halting IUU fishing (efficiency=1) could potentially increase fish biomass by 118.34%*31% = 36.69%. For comparison purposes with other shoots, we chose to adopt the same colour scale (RHColor=100). This means that for the maximum effectiveness (IUU risk = 0), we would reach the maximum biomass of fish in the sea, when 100% stocks are exploited at MSY or below. However, we warn that actually, this would represent only ca. 35-40% of virgin biomass (i.e. fish biomass without fishing) ¹¹.

For the fisheries catch indicator (Figure 2C, right shoot), we also considered the gains provided by an increase in management effectiveness ( combatting IUU fishing). Costello et al. ¹⁰ suggest that applying FMSY to all stocks would increase fisheries catch by 17% compared to current situation, an estimate which is in agreement with Ye et al. ¹² who estimate this rate at 16.5%. Again, if we consider that IUU fishing concerns overexploited stocks (31% of global catch as an upper estimate), halting IUU could potentially increase fisheries yield by 117%*31% = 36.27%. At maximum effectiveness, we would reach the maximum fisheries catch, considering 100% stocks are exploited at MSY, and no IUU fishing.

**1.3 Food consumption** (see also Supplementary Table S3)

Along the y-axis, transitions between different diets are characterised by the percentage of protein from plant-based foods in a country's average diet. There are 29 countries with a plant-based protein fraction higher than 75%, accounting for a population of 2.57 billion (with the majority of those people in India with 75.4% of protein from plant-based foods), whereas in 31 countries with a total population of 0.93 billion the protein intake would be on average 37.5% or less from plant-based foods (e.g., Brazil (36.9%), USA (31.3%); see Supplement). The 2021 global weighted average is 58.8% protein is from plant-based sources. The total protein (from animal and plant foods) in the diet is kept constant, so moving up the y-axis increases the plant-based foods and reduces animal products intake, with a vegan diet representing 100%, and conversely moving down increases the animal products and reduces plant-based foods consumed. The types and proportions of food within the plant and animal sourced categories are held constant for each country. Commodities' protein contents were obtained from FAO food supply ¹³. We explore the range 30-100% for percentage of plant-based protein, with the observed range being from 29.4% (Estonia, the only country below 30%) to 92.3% (Burundi). The plant-based fraction was plotted as an index (0 to 1) across this range, making 0 represent 30% and 1 and 100% plant protein in the diet.

Regarding consumption efficiency on the x-axis, the data from FAOSTAT do not allow differentiation between discarded food and overconsumption. For example, in the situation where 80 g/capita/day of protein was supplied, rather than the nutritionally required 52g/capita ¹⁴,¹⁵, the consumer efficiency would be 65% (i.e. 52/80 = 0.65). This would arise if, e.g., 35% of the food supply is either discarded, or overeaten (80 g capita/day * (1-0.35) = 52 g capita/day). Consumption efficiency could also be reduced by combinations of both (food supply being discarded and food eaten beyond nutritional requirement for protein). For example, if 25% of the 80 g capita/day protein supply was discarded then 60 g/capita/day would be eaten (i.e. 80 g capita/day * (1-0.25) = 60 g/capita/day), and 13% of this eaten protein would be above what is nutritionally required (i.e. (60-52)/60 = 0.13).

Values of consumption efficiencies above 1 (or 100%) indicate insufficient supply of protein, which is reflected in the observed range of country consumer efficiency from 35.7% (Iceland, the only country below 40%) to 182% (Democratic Republic of the Congo, with only 28.5g of protein supply per person per day; not shown in Figure S1). There are further 10 countries with an efficiency over 100%, indicating that there is insufficient protein supply
on average in those countries. But a value of 100% (or below) can also represent a case where food is not equitably distributed, and potentially including undernourishment for some. In countries with undernourished individuals due to distributional inequalities, over-consumption and waste in the rest of the population is understated. To address this, FAO data for number of people undernourished per country ¹⁶ was used to estimate efficiencies excluding people who are undernourished. Adjusted efficiencies were then calculated for the remaining (not undernourished) population, with food supply for this group calculated assuming that the undernourished group would have required 50% additional protein to achieve the required protein levels. For instance: country x has a population of 30 mio people and a per capita protein consumption of 40g, hence an efficiency of 1.3. The total population consumes 1200 Mg d⁻¹. However, according to FAOSTAT 15 mio. people are undernourished. Assuming these are eating 50% below requirements they are consuming 390 Mg d⁻¹ of protein. This means that the non-undernourished part of the population (15 mio) consumes 810 Mg d⁻¹ (or 54 g d⁻¹ on average per capita) with an efficiency of 0.96.

The consequences of efficiencies adjusted for undernourishment were explored in the analysis across the range between 40% and 100%, which encompasses countries containing 99.1% of the global population (Figure S1). The 2021 global weighted average is efficiency is 57.3% without adjusting for undernourishment and 53.4% including that adjustment. In Figure XXX, the effectiveness plotted is an index (0 to 1) across this range of consumer efficiency, 0 representing a consumer efficiency of 40% and 1 and 100% efficiency (see also Shoots_Nexus.xls).

<figure><img src="image_12.png" /></figure>
<figure><img src="image_13.png" /><figcaption>Figure S3: Countries plotted on food consumption surface, for the year 2021, excluding DR Congo (upper panel). Lower panel: Consumption efficiency (the combined effect of discarded foods and over-consumption, with over-consumption taken as intake over that required for human nutrition) -adjusted for undernourishment.</figcaption></figure>

Agricultural area required to produce the food and feed consumed was used as a proxy to terrestrial biodiversity impacts. These areas were estimated from the required production at each point on the plant-based protein percentage versus consumer efficiency surface and global average yields for each commodity ¹⁷. The approach followed Alexander et al. ¹⁸ who used the reported country-level statistics for cropland area, production quantities, commodity uses and nutrient values to compute average production and efficiency for primary crops and processed commodities to calculate agricultural area needed to meet a certain diet. Animal products were included considering both cropland areas used to produce animal feed and pasture area ¹⁸. The areas are then translated to a sustainability scale using a planetary boundary of 25% of the land surface used for agriculture ¹⁹ as a neutral score of 0. Of the agricultural area 7% is assumed to be unharvested due, e.g., to being fallow or failed crops, that leads to lower annually harvested area relative to total cropland despite multi-cropping in some locations ¹⁷,²⁰,²¹. Biodiversity impacts are lowest (approaching a colour scale of 100, see Shoots_Nexus.xls) at a predominantly plant-based diet, and high efficiencies. Whether or not a (theoretical) globally 100% vegan diet would also have some detrimental impacts on biodiversity is unclear; in some regions, low impact grazing maintains open landscapes and higher species-diversity but experimental studies also show diverse impacts of low grazing intensity on biodiversity, depending on the grazed ecosystem, the climate environment, grazing species and biodiversity indicator assessed ²²⁻²⁴. In our assessment we do not assume that a (theoretical) fully vegan world would reduce the overall positive impact of plant-based diets on biodiversity but note that this assumption comes with very high uncertainty and may be too optimistic.

An analogous approach was taken for climate regulation, where GHG emissions associated with the production of agricultural commodities was used. GHG emissions associated with the diet implied by combinations of plant-based percentage and consumption efficiency were calculated as the sum of the emissions associated with each food. The individual food emissions were determined as the product of quantity of each food in the diet and the associated median emission factors from Poore & Nemecek (²⁵). A vegan diet and 100% consumer efficiency is associated with the lowest agricultural GHG emissions on the food consumption surface, corresponding to a 67% reduction in current agricultural emissions. This was given the highest score (of 100). A 30% reduction in GHG emissions was aligned with a neutral score (of 0) ²⁶. The impact of dietary choice on GHG emissions is highly dependent on efficiency (Shoots_Nexus.xls).
## 2. Supplementary Tables

Tables S1 – S5 provide additional literature that underpins *Figures 2* and *3* in the manuscript. The data summarised in these tables was used to support colour settings and transitions in these figures.

'Indicator' specifies whether the literature source was used to assess impacts on biodiversity (BD) or ecosystem services (ES); 'Scale' specifies the geographic extend of the literature source; 'Method' provides some information of the methodology of the source; 'Results' provides the main results extracted from the literature, separated by whether these were mainly used to support colour settings on the y-axis or x-axis (or both).
<table><caption>Table S1: Food production on land</caption><thead><tr><td>Indicator</td><td>Scale</td><td>Method</td><td>Results, relevant to y-axis</td><td>Results, relevant to x-axis</td><td>Source</td></tr></thead><tbody><tr><td>Yield gap</td><td>Global</td><td>Statistics</td><td></td><td>Large production increases (45% to 70% for most crops) possible from closing yield gaps to 100% of attainable yields; necessary changes to management practices vary considerably by region and current intensity.</td><td>4,27</td></tr><tr><td>Fertilizer application rate</td><td>Global</td><td>Statistics</td><td>National fertilizer application rates (2012 to 2016)</td><td></td><td>28</td></tr><tr><td>Nitrogen use efficiency (incl. N surplus)</td><td>Global</td><td>Review</td><td>Current N surplus in total: 100Tg N yr<sup>-1</sup> (eq. 100Mt N yr<sup>-1</sup>)</td><td></td><td>3</td></tr><tr><td>Nitrogen use efficiency</td><td>Global</td><td>Statistics</td><td>Currently, only 47% of reactive nitrogen added globally onto cropland is converted into harvested products, compared to 68% in the early 1960s, while synthetic N fertilizer input increased by a factor 9 over the same period.</td><td></td><td>2</td></tr><tr><td>Cropland expansion rates</td><td>Global</td><td>Statistics</td><td>National cropland expansion rates (2000 to 2016)</td><td></td><td>20</td></tr><tr><td>Biodiversity</td><td>Global</td><td>Review/ Assessment</td><td>Species richness higher and species abundance rate lower for more natural land use system compared to system impacted by humans</td><td></td><td>29</td></tr><tr><td>ES (Yield growth rates)</td><td>Global</td><td>Review/ Assessment</td><td>Cereal yield increase since 1961 by 240% (until 2017)</td><td></td><td>30,31</td></tr><tr><td>ES (Yield growth rates)</td><td>Global</td><td>Statistics</td><td>Between 1985 and 2005, the total global crop production increased by 28%: through a 2.5% net expansion of global cropland area, an 7% increase in the frequency of harvesting, and an average 20% increase in crop yields per hectare</td><td></td><td>32</td></tr></tbody></table>
<table><tr><td>Future cropland expansion, GHG emissions from agriculture</td><td>Global</td><td>Statistics, Modelling (GlobAgri-WRR model)</td><td>Difference between the amount of food produced in 2010 and the amount necessary to meet likely demand in 2050 estimated as 56% more crop calories than were produced in 20doi: 10.<br/>Difference between global agricultural land area in 2010 and area required in 2050 (even if crop and pasture yields continue to grow at rates achieved in the past), estimated as ca. 590 million hectares.<br/>Difference between the level of annual GHG emissions from agriculture and land-use change in 2050 (15 Gt), and a target of 4 Gt that represents agriculture's proportional contribution to holding global warming below 2°C above pre-industrial temperatures.</td><td>33</td></tr><tr><td>ES (Emissions from agriculture)</td><td>Global</td><td>Review/ Assessment</td><td>Table SPM1: N<sub>2</sub>O emissions from agriculture: 8 +/- 2Mt N<sub>2</sub>O a<sup>-1</sup> (2.2 +/-0.7 Gt CO<sub>2</sub>eq a<sup>-1</sup>). 8 +/- 2Mt N<sub>2</sub>O a<sup>-1</sup> are ca. 8% emissions from all N surplus.</td><td>30</td></tr><tr><td>ES (Emissions from agriculture)</td><td>Global</td><td>Multi-model study</td><td>Increase in N<sub>2</sub>O emission increase attributed to fertilizer application, N deposition, manure N application: 54%, 26%, 15%, respectively, in recent decade; ca. one third of total soil emissions</td><td>34</td></tr><tr><td>ES (Carbon cycle impact)</td><td>Global</td><td>Review, multi-model analysis, compare studies with vs. without land-use change</td><td>Future land-use change (RCP 8.5 scenario) dampens projected land carbon storage by ca. one quarter and enhances disturbance-related carbon loss by nearly 50%.</td><td>35</td></tr><tr><td>ES (Carbon cycle impact)</td><td>Global</td><td>Simulation study, dynamic global vegetation models + land-use change projections</td><td>Land-use change impacts on total ecosystem carbon pools negative, with large differences between SSP/RCP combinations, and between three</td><td>36</td></tr></table>
<table><tr><td></td><td></td><td></td><td>DGVMs (range: -20 to -215 GtC by end of 21st century)</td><td></td></tr><tr><td>ES (Carbon cycle impact)</td><td>Global</td><td>Simulation study, Earth system model + land-use change projections</td><td>Land-use change impacts on total ecosystem carbon pools variable, with large differences between SSP/RCP combinations (range: -25 to 45 GtC by end of 21st century)</td><td>37</td></tr><tr><td>ES (Carbon cycle impact)</td><td>Global</td><td>Simulation study, Earth system model + land-use change projections</td><td>Land-use change impacts on total ecosystem carbon pools variable, with large differences between SSP/RCP combinations (range: -93 to 23 GtC by end of 21st century)</td><td>38</td></tr></table>
<caption>**Table S2: Production, marine fisheries**</caption>

F: fishing mortality rate; MSY: Maximum Sustainable Yield; Fmsy: fishing mortality rate at MSY; B: fish biomass in the sea; Bmsy: fish biomass in the sea when exploited at a rate equal to Fmsy; Bmey: fish biomass in the sea when exploited at a rate equal to maximum economic yield; B0: pristine fish biomass (no fishing); IUU: Illegal, Unreported, Unregulated fisheries; BAU: business as usual; RBFM: Rights-based fisheries management.

<table><thead><tr><td>Indicator/ comments</td><td>Scale</td><td>Method</td><td>Results, relevant to y-axis</td><td>Results, relevant to x-axis</td><td>Source</td></tr></thead><tbody><tr><td>Fisheries yield</td><td>Global</td><td>Meta-analysis</td><td>In 2022, 79.7 M tons of fish have been landed from marine capture fisheries. Global catches have been leveling off around 80 M tons since the late 1980s.</td><td></td><td>7</td></tr><tr><td>Exploited stocks</td><td>Global</td><td>Meta-analysis</td><td></td><td>The proportion of fish stocks that are fished within sustainable levels decreased from 90% in 1974 to 62.3% in 2021.</td><td>7</td></tr><tr><td>Fish biomass</td><td>Global</td><td>Data analyses, reconstruction of data time series</td><td>The 1970s saw the first collapses of major fisheries stocks –at the time when the rate of increase in global fishing effort began to take off sharply – and the decreasing catches per unit effort reach an inflexion point in many parts of the world</td><td></td><td>39,40</td></tr><tr><td>Fisheries yield</td><td>Global</td><td>Stock assessment models</td><td>Rights-based fisheries management (optimization of economic value) leads to 98% stocks sustainably exploited. BAU leads to a further 20% of stocks overexploited. Rebuilding fish stocks under RBFM could generate annual increases exceeding 8 M tons compared to current levels in catch, 37 billion $ in profit, and ~300 M tons of fish biomass compared to current levels. Fishing at Fmsy would increase the yield by doi: 10.7 M tons (by 17%).</td><td></td><td>41</td></tr><tr><td>Fish biomass</td><td>Global</td><td>Stock assessment models</td><td>Overall, they estimate the global median fishing mortality is F/Fmsy=1.5 (overfishing is occurring) and biomass is B/Bmsy =0.78 (stocks are overfished). They estimate that applying Fmsy to all stocks of conservation concern would increase fish biomass at 994.4 million tons (current biomass is</td><td></td><td>41</td></tr></tbody></table>
<table><tr><td></td><td></td><td></td><td>estimated at 840.3 million tons in early 2010s). So this represents an increase of 18.34% in fish biomass.</td><td></td></tr><tr><td>Fisheries yield</td><td></td><td></td><td>Rebuilding overfished stocks to the biomass that enables them to deliver MSY could increase fisheries production by 16.5 million tonnes and annual rent by US$32 billion.</td><td>12</td></tr><tr><td>Exploited stocks, IUU</td><td>Global</td><td></td><td>In the early 2000s, IUU fishing represented between 11 and 26 million tonnes, ca. 23% (13.4-32%) of the fisheries catch. US: between 4-15% NW Atlantic, 1-4% NE Pacific</td><td>8</td></tr><tr><td>Fisheries yield</td><td>European seas</td><td>Stock assessment models (bayesian surplus production models)</td><td>Exploitation levels of 50-80% of the maximum will rebuild stocks and lead to higher catches than currently obtained, with substantially higher profits for the fishers. At present, 64% European fish stocks are overexploited (2013-2015). Less than 20% of European Mediterranean fish stocks are exploited sustainably, with a biomass above the one that can produce MSY. Under Fmsy strategy, catches increase from 9.4 M tons in 2017 to 13.7 M tons by 2030. The mean profitability would be at about 50% above the 2014 value.</td><td>42</td></tr><tr><td>Fisheries yield</td><td>multiple national fisheries</td><td></td><td>Low stock abundance has 2 major negative effects: the sustainable yield is lower, so fisheries contribute less to food production and the cost of harvesting often rises when abundance is low. Excess fishing pressure now accounts for about 3% to 5% loss of potential yield from the stocks included in the paper (constituting half of world marine catch). The global loss of potential yield is probably much higher as the non-included fish stocks are those with poor data, are unassessed and unmanaged.</td><td>43</td></tr></table>
<table><tr><td>Fisheries yield, IUU, biomass</td><td>Mediterranean sea</td><td>Stock assessment models; observational data</td><td>75% overexploited stocks</td><td>IUU: ca. 20% catch</td><td>44</td></tr><tr><td>IUU</td><td>Global and national level</td><td>Meta-analysis</td><td>Develops the IUU Fishing Risk Index, a composite indicator that ranges from 1 (good/strong) to 5 (bad/weak). In 2023, the world overall score is 2.28. The score for the Mediterranean sea is 2.23 whereas the US stands at 2.32. The best-performing country is Romania (1.62) while the worse-performing country is China (3.69).</td><td></td><td>9</td></tr><tr><td>Stocks</td><td>Mediterranean sea</td><td>Stock assessment models; observational data</td><td></td><td>58% overexploited stocks</td><td>45</td></tr><tr><td>Stocks</td><td>US</td><td>Stock assessment models; observational data</td><td></td><td>19% overfished stocks in 2022</td><td>46</td></tr><tr><td>Biomass</td><td>general</td><td>Stock assessment</td><td>Bmsy/B0 in the order of 0.35-0.4<br>Bmey/B0 in the order of 50-60%</td><td></td><td>11</td></tr></table>
<table><caption>Table S3 Consumption</caption><thead><tr><td>Indicator</td><td>Scale</td><td>Method</td><td>Results, relevant to y-axis</td><td>Results, relevant to x-axis</td><td>Source</td></tr></thead><tbody><tr><td>Key BD areas</td><td>Global</td><td>Global economic model &amp; spatial mapping approach</td><td>Consumption of animal products accounts for more than half of biodiversity loss within KBAs; bovine meat production in KBAs is the largest single contributor to this loss (ca 31% of total biodiversity loss). Lightly grazed pasture contributes around half of all potential species loss, 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).</td><td></td><td>47</td></tr><tr><td>BD hotspots</td><td></td><td></td><td></td><td>Eliminating global food loss and waste would lessen nitrogen critical load exceedances in BD hotspots by up to 19% (for 2015).</td><td>48</td></tr><tr><td>Trophic levels</td><td>Global</td><td>Meta-analysis</td><td>Livestock has positive impact on plants and primary consumers (richness, abundance, biomass) and is neutral for secondary consumers. Livestock does not impact plants (richness, abundance, cover), except for plant biomass. Impact of type of livestock (grazers, browsers) very diverse, and no clear difference emerges.</td><td></td><td>23</td></tr><tr><td>Species diversity</td><td>Europe</td><td>Review &amp; meta-analysis</td><td>Low-intensity of use positively affects species grassland diversity in Europe. Abandonment results on overall BD loss, with largest impacts on plants while impacts on birds and vertebrates were visible but non-significant statistically.</td><td></td><td>22</td></tr><tr><td>Various</td><td>Global</td><td>Review</td><td>Livestock footprint: 1/3 of global cereal production, ca. 40$ of arable land, appropriates 2 bio ha grassland (of which</td><td></td><td>49</td></tr></tbody></table>
<table><tr><td></td><td></td><td></td><td>around one third could be used as cropland.</td><td></td><td></td></tr><tr><td>Various</td><td>Global</td><td>Review</td><td>Dietary changes need to be looked at region-specific (including to account for a more just distribution of intake). Impact of diet on BD can be variable – either through local production in countries with high species richness and small share of food imports (e.g. tropical Central America) or large per-capita consumption paired with imports from BD rich countries (e.g. North America, EU).</td><td>Environmental impact of food consumption can be lessened in some regions considerably by reducing overconsumption.</td><td>50</td></tr><tr><td>Various</td><td>Global</td><td>Review</td><td>Meat has much larger environmental and climate footprints than plant-based foods and can also be associated with negative health effects. But in low income countries nutritious plant-based foods not always available or affordable. Animal-sourced foods responsible for up to two-thirds of all food-related GHG emissions and core driver of biodiversity loss. Several studies conclude that livestock production is one of the leading causes of global biodiversity loss, although meat production can also cause some positive externalities for wild animals and plant biodiversity.</td><td></td><td>25,51,52</td></tr><tr><td>Various</td><td>Global</td><td>Review</td><td></td><td>Farm stage food waste produces the equivalent of 2.2 Gt CO2-eq annually and requires the equivalent of 4.4 mio km2 land.</td><td>53</td></tr><tr><td>Various</td><td>Global</td><td>Review</td><td>Silvopastoral systems provide efficient feed conversion, with co-benefits regarding biodiversity and habitat connectivity and maintenance of carbon stocks.</td><td></td><td>54</td></tr></table>
<table><tr><td>GHG emissions</td><td>Global</td><td>Data-based, cross-sector estimate combined with modelling</td><td>Global GHG emissions (ca. 2010) from the production of food: 17,318 ± 1,675 TgCO2eq yr-1, of which 57% from the production of animal-based food (including feed). Farmland management and land-use change represented major shares of total emissions (38% and 29%, respectively); rice and beef were the largest contributing plant- and animal-based commodities (12% and 25%, respectively).</td><td>55</td></tr><tr><td>GHG emissions</td><td>Global</td><td>Economic (general equilibrium) modelling</td><td>Economic spillovers of the ‘eat-Lancet’ diet into non-food sectors (as a result of changed food prices and altered incentives for consumers and producers) reduce the GHG emission gains associated with dietary changes.</td><td>56</td></tr><tr><td>GHG emissions</td><td>Global</td><td>Review</td><td>While measures to reduce food waste and losses decrease the land footprint of food production notably, some of these gains are offset by the energy required to reduce these losses (e.g. refrigeration, processing, transport).</td><td>57</td></tr><tr><td>GHG emissions</td><td>Global</td><td>Review</td><td>C footprint of food produced but not eaten is (without accounting for GHG emissions from land use change) is ca 3.3 Gt CO2-eq; if a country, food wastage would be the third top emitter after USA and China.</td><td>58</td></tr><tr><td>GHG emissions</td><td>US</td><td>Data-based estimate combined with dietary risk index and LCA</td><td>Reduction of dietary carbon footprint by 33% in response to substituting 10% of daily caloric intake from beef and processed meat by fruit, vegetables, nuts and selected seafood.</td><td>59</td></tr></table>
<table><caption>Table S4: Forest restoration, reforestation and afforestation</caption><thead><tr><td>Indicator</td><td>Scale</td><td>Method</td><td>Results, relevant to y-axis</td><td>Results, relevant to x-axis</td><td>Source</td></tr></thead><tbody><tr><td>Current area, C-pools/ fluxes</td><td>Global</td><td>Forest area and age map + DGVM</td><td>Area: 16.5 mio km² old growth, C uptake: 0.85 (0.66-0.96) Pg a⁻¹ in old growth forest (&gt;140 years old in 2010), 40%.</td><td>Forest carbon sink (2001-2010): 2.15 (1.89-2.81) PgC a⁻¹, of which 1.3 (1.03-1.96) Pg in regrowing stands (60%).<br>Area: 26.3 mio km² regrowth.<br>Sink due to env. change (all forest) 1.36-1.43 PgC a⁻¹; C-budget (residual): 2.4 +-0.9.</td><td>60</td></tr><tr><td>Current area, C-pools/ fluxes</td><td>Global</td><td>Empirical estimates based on compilation of different data sets</td><td></td><td>Current total live biomass: 450 (407-476) PgC, potential: 916 PgC; ca. 55% of this difference due to deforestation (256 PgC). Forest live biomass: ca. 300-370 PgC (potential on existing area: 440-460 PgC), which is ca. 75% (350/450) of total biomass.</td><td>61</td></tr><tr><td>Current area</td><td></td><td></td><td>Intact/primary forest: 12 (11.7-12) mio km². → 30% of today's total forest area remains more or less untouched.</td><td>Used forest 28 (20.3-30.5) mio km²; plantation: 3 mio km², the remaining area of different management intensity.</td><td>31</td></tr><tr><td>BD (ES)</td><td>Savanna/grassland</td><td>Review + Geospatial mapping analysis</td><td>In cerrado system, conversion of savanna to forest results in net loss of species (up to 30%; e.g., shrub and herbaceous, ants). Trade-off between species richness and C-storage. Each 1% increase in ecosystem C (forests 3-4 times C pools compared to savanna) corresponds to 0.27% loss of plant species and 0.43% decline in ant species. Trees also require more water and nutrient. More generally, carbon changes uncertain due to large storage of C in grassland soils.</td><td>23 mio km² potentially available for reforestation (using WRI data, to confirm WRI/IUCN Atlas numbers, 20 mio km²), of which however ca. 40%, 9.3 mio km², are in fact naturally grassy biomes and should not be considered → 23 - 9.3 = 13.7 mio km² (or 20 mio * 0.4 = 12 mio km²).</td><td>62<br>63</td></tr><tr><td>BD</td><td>Europe</td><td>Review</td><td></td><td>Plantation forests: in addition of being BD poor (or at least poorer than native) there is also a risk of planted species being invasives.<br>Globally, around 25% of planted forest area is with non-native species, in 2015 ca. 278 mio ha forest planted.</td><td>64</td></tr></tbody></table>
<table><tr><td>Biodiversity + ES</td><td>Global</td><td>Review & Meta-analysis, natural vs. active regeneration.</td><td>Plantations generally sequester more C faster in biomass (but this is not the same as total ES uptake), although in dry regions where native trees may be more competitive.<br/>Natural regeneration found in most cases to be most effective for restoring Biodiversity, but planting native species also good for Biodiversity. Across field studies, restoration success was 34-56% (Biodiversity, plants, birds invertebrates) and 19-56% (ecosystem structure, cover, density, litter, biomass, height) higher in natural regeneration than active restoration.<br/>Success depends on time since restoration effort started, past land use, size of area (more habitat). Different Biodiversity indicators and indicators of function recover at different speed (e.g., abundance more rapidly than diversity; bgf took longer and did not recover fully; vegetation structure and indicators such as species similarity and composition likely to take much longer than abundance and richness).<br/>Species diversity (and other indicators of Biodiversity) usually lower than in old-growth forests. For instance, one meta-analysis (Crouzieilles et al. 2016) showed that across 221 studies, restoration enhanced Biodiversity by 15-84% and vegetation structure by 36-77% compared w degraded sites (still below old-growth levels). Differences between reference sites and restored systems for Biodiversity lower than for structure (Biodiversity recovered more fully). Largest success if within an area the reforestation areas include diversity of habitats. Low impact management (little/no harvest) fosters Biodiversity.</td><td>65, 66, 67, 68</td></tr><tr><td>Biodiversity + ES (climate)</td><td>Global &amp; Brazilian Atlantic</td><td>Geospatial analyses (mammal, amphibian, bird distribution) and (for Brazilian Atlantic) Spatial prioritisation approach + account for ecological (Biodiversity, ag. C sequestration for 20 years after restoration) and economic aspects of restoration. Aim to maximise benefits (e.g., reduction of projected extinctions owing to habitat gain) while minimise costs (e.g., loss of</td><td>Currently, 27-33% of species committed to extinction, 73-84% of natural vegetation cover lost. Restoration target, 5.2 mio ha equivalent to 4% of the natural biome area.<br/>Baseline: farmers each restore 4% of area in their individual farms: worst for Biodiversity, 4<sup>th</sup> worst for C and most expensive.<br/>Fewer, larger (area) projects tend to have win:win regarding reduced costs and gain for Biodiversity as well as C. Biodiversity focussed allocation has large c-benefit also for C sequestration (but not vice versa): 30% extinctions avoided, 1 Pg CO2eq. after 20years; “Compromise”: 25% and 1 Pg CO2eq. when considering cost effectiveness; (taken costs out: nearly 30% extinctions avoided, 1.1 Pg CO2eq.)<br/>Globally, large co-benefits possible between avoiding Biodiversity and carbon loss by avoiding further deforestation (global rank correlation coeff. for C-stock vs. species richness = 0.82)</td><td>69, 70</td></tr></table>
<table><tr><td></td><td></td><td>agric. revenue, costs of active restoration) of restoration.</td><td></td><td></td></tr><tr><td>Climate ES &amp; Biodiversity</td><td>Tropical forest regions</td><td>Meta-analysis (69 studies), restored, degraded, reference sites. Divers indicators analysed (such as richness, density, abundance, element cycles and pools)</td><td>Restoration increases ecosystem function relative to degraded areas, but not relative to reference sites. Effect size on C-pools on restored sites: c. 0.52, Biodiversity protection: 0.53 Did not find a clear time-since-restoration-started signal. In all cases, Biodiversity and ESF below reference.</td><td>"Natural regeneration" has most positive effect on ESF (total effect size 91% compared to degraded). Reforestation with exotic species showed no ESF difference compared to degraded sites. Effect size of planting natives: 34%.</td></tr><tr><td>Biodiversity (ES)</td><td>China</td><td>Meta-analysis of several hundred published studies; sets of different indicators for Biodiversity and ecosystem function (mostly element cycles).</td><td>Biodiversity and ESF indicators higher in restored than degraded forests 22-196% and 11-319%), but remain lower (10-15% and 19-65%) compared to reference forest, even after several decades; Biodiversity recovered bit more fully than ESF. Grain-for-Green forests are mostly monocultures or at best simple mixed forests (2-5 species). At reforestation sites (previous: agriculture) less biodiversity compared to native forests.</td><td>Enhancement of Biodiversity and ESF was larger in unassisted recovery vs. areas w active restoration measures</td></tr><tr><td>Biodiversity, ES (climate &amp; material)</td><td>Global</td><td>Review: regeneration, plantation, agroforestry</td><td>Biodiversity indicators often remain lower than old-growth even for decades and centuries. Likewise, even if ag. C sequestration is rapid, total C pools remain below old-growth for long periods. Plantations generally have lower Biodiversity; while C sequestration in agb can be high.</td><td>Natural regeneration highest levels of Biodiversity and biomass (but less than primary forest) but fewer provisioning/material services.</td></tr><tr><td>ES, climate</td><td>Regional studies (global coverage)</td><td>Review plot studies in restoration forests</td><td>Planted forests (estimates based on growth curves): 4.5-40.7 t CO₂ ha⁻¹ a⁻¹ during first 20 years of growth. Monocultures (and plantations with few different species only) can provide efficiently services related to C storage, erosion control, soil fertility</td><td>Natural regeneration: 9.1-18.8 t CO₂ ha⁻¹ a⁻¹ (rate remains or increases after the 1st 20 years of growth). A wider variety of ecosystem function arises from multiple species especially if diversity in traits is achieved.</td></tr></table>
<table><tr><td></td><td></td><td></td><td>improvement or habitats for habitat generalists.</td><td></td></tr><tr><td>ES (climate)</td><td>Global</td><td>Review/assessment</td><td>All by 2030:<br/>Avoided forest conversion: 2.9-4 Gt CO<sub>2</sub> a<sup>-1</sup><br/>Reforestation (excludes areas where forests are not native cover, using a threshold of 25% trees rather than 10% used in FAO and Atlas) 2.7-18 Gt CO<sub>2</sub> a<sup>-1</sup>.</td><td>77</td></tr><tr><td>ES (climate)</td><td>Global</td><td>Review, no separation possible between afforestation and reforestation in AR studies</td><td>Cumulative AR potential until 2100: 80-260 Gt CO<sub>2</sub>, much higher without addnl. constraints (&gt;800 Gt CO<sub>2</sub>). Up to 0.5-7 (2050) (10-12 in 2100) Gt CO<sub>2</sub> a<sup>-1</sup>. AR cost effective (competitive &lt;&lt; 50 $ per t CO<sub>2</sub>). 0.5 Gt CO<sub>2</sub> assessed as the sustainable potential (IPCC).<br/>Sink in regrowing forests will saturate and issues of permanence (risk of loss) to be factored in. Albedo impacts in boreal (and likely temperate) will constrain success of AR to tropics. 500 mio ha (tropics) are feasible but ambitious (3.6 Gt CO<sub>2</sub> a<sup>-1</sup> by around 2065 and declining afterwards).</td><td>78, 79</td></tr><tr><td>ES (climate, food)</td><td>Global</td><td>Integrated Assessment Model (MagPie/Remind). Different scenarios of agricultural expansion, forest protection, afforestation/reforestation, yield increase.<br/>2<sup>nd</sup> gen. 100 EJ a<sup>-1</sup> in 2055, 300 EJ a<sup>-1</sup> in 2095 (Misc.). 70 and 250 EJ a<sup>-1</sup> in scenario with avoided deforestation.</td><td>Globally unrestricted AR: increase food prize &gt;80% despite of annual 1.66% yield increase in 2050; AR only in tropics (in order to avoid albedo effects): increase in food prize in medium term buffered by increase in GDP (globally, but regional inequities unclear).<br/>Forest conservation also leads to higher food price than in a reference scenario, between ca. +5% in 2055 and +70 or +50%. Has also large impact on water price (&gt;300 %).</td><td>80, 81</td></tr></table>
<table><caption>Table S5: Bioenergy</caption><thead><tr><td>Indicator</td><td>Scale</td><td>Method</td><td>Results, relevant to y-axis</td><td>Results, relevant to x-axis</td><td>Source</td></tr></thead><tbody><tr><td>Present values</td><td>Global</td><td>Statistics &amp; review of case studies</td><td>Ca. 45-55 EJ a<sup>-1</sup> total, most of it woodfuel for heating (ca. 50 EJ a<sup>-1</sup>). Dedicated biofuel estimated as (2011-12) 2.5 – 3.2 EJ a<sup>-1</sup>. In many regions traditional wood fuel harvesting is unsustainable (ie above the biomass increment from (re)growth), but exact numbers highly uncertain, estimates around 10-35% being unsustainably sourced.</td><td></td><td>82, 83, 84, 85, 86, 87</td></tr><tr><td>Biodiversity (ES)</td><td>Global and large regional</td><td>GIS: Spatial overlap<br/>Compare core areas for expansion of PA network (based on species range maps) with energy potential of Miscanthus (yields from crop model) on that same land.</td><td>Total BE potential estimated in current PA: ca. 37 EJ; expansion to 17% PA: additional ca. 42 EJ: 30% PA: additional ca. 35 EJ, Land outside top 30% priority ca. 30 EJ. BE expansion major risk to Biodiversity. Globally, half of the BE energy potential located in the top Biodiversity areas (ie land that would be prioritised if PA expanded to 30%). ¾ of which is land currently unprotected, large part of which in tropical regions.</td><td>Areas identified for BE also in conflict w. land identified as important for closing yield gaps.</td><td>88</td></tr><tr><td>Biodiversity (ES)</td><td>Global, studies range from country to continent (EU, US) to globe.</td><td>Reviews, mostly based on observations (but also scenario and other reviews).</td><td>Impacts depend on initial land use and are mostly negative for Biodiversity, esp. in tropics (in one review 88% of studies report negative impacts; highest negative effects for soy in Brazil and oilpalm in Indonesia, 87% &amp; 35% decline in natural capital index (indicates original species</td><td>Impacts less negative for 2<sup>nd</sup> gen BE (in one review 87% of studies on 1<sup>st</sup> gen. and 27% of studies on 2<sup>nd</sup> gen. report negative impacts), esp. in temperate zones and esp. if planting occurs on land previously already used for agriculture (but care has to be</td><td>89, 90, 91, 92</td></tr></tbody></table>
<table><tr><td></td><td></td><td></td><td>composition)). Majority of concerns in general arise from studies that investigated conversion of natural vegetation for BE. Include not only direct replacement of nat. vegetation, but also indirect effects.<br/>Biofuels only reduce emissions to the extent that their growth and land management can result in C sequestration and storage over and above what would have occurred in the absence of biofuel production.<br/>Large uncertainties arising from unknown invasiveness potential of BE crops. Likewise, current availability of “surplus” land (not used for food crop etc) is unknown. Overall, number of studies that investigate BE-Biodiversity are very limited.</td><td>taken w.r.t. “marginal” lands). Perennial (2<sup>nd</sup> gen) crops in general provide a bit more diverse habitats, esp if native perennials, and using low-input species. Positive impacts shown in some 2<sup>nd</sup> gen. studies, esp. for grasses and short-rotation coppice in Europe.<br/>Miscanthus needs good water supply, as deep roots, large leaf area, productive – high ET to support productivity, yields can decline in water shortage considerably.</td><td></td></tr><tr><td>Biodiversity</td><td>Global</td><td>Several climate-based SDM (amphibians, birds, mammals). Compare RCP 2.6 with low climate impacts and large BE crop expansion and RCP 6 with large climate change and basically no BE crops. (ca. 100-200 EJ a<sup>-1</sup> in 2050 (dep on scenario); by 2080 ca. 250 EJ a<sup>-1</sup> in SSP2/RCP2.6)</td><td>Low CC scenarios with large BE crop area (2080): negative impacts of LUC on Biodiversity not compensated by reduced negative CC impacts. Likewise, at high CC scenario, negative CC impacts relatively larger than the negative LUC impacts. Both scenarios not good for vertebrate Biodiversity. Basically, when CC and BE taken together, same number of species (ca. 15000) affected negatively.</td><td></td><td>93</td></tr><tr><td>Biodiversity (area), ES (climate)</td><td>Global</td><td>IAM; strong focus on zero net deforestation in accordance to WWF Living Forest Report; total of 5 scenarios w. varying degree of BE vs. conservation measures.</td><td>If population growth and consumption continue as projected, it is not possible to conserve natural areas, halt forest loss, have large amounts of renewable energy simultaneously.<br/>Only one scenario (BEplusRED) stops deforestation. In all scenarios the “sustainable” management of forests is allowed, so area of managed forest increases (up to 300 mio ha in BEplusRED).</td><td></td><td>94</td></tr></table>
<table><tr><td></td><td></td><td>Mix of 1<sup>st</sup> and 2<sup>nd</sup> generation BE. Only 1st gen. BF from food crops; heat, power and 2<sup>nd</sup> gen. liquid BF all mostly from wood in their model (from natural forests or short-rotation forestry).</td><td>Even a scenario that targets forest protection (and regrowth) while supplying addnl. BE leads to reduction of grassland and other natural vegetation for BE crops. In all cases GHG emissions from deforestation ie reduced climate efficiency of BF. Total GHG emissions from all land use: +ca. 20% CO2 in 2050 in BEplusRED/BiodivRED to +ca. 80-100% CO<sub>2</sub> in the other scenarios.</td><td></td></tr><tr><td>Biodiversity, ES.</td><td>Global</td><td>Modelled spatially explicit estimate of sustainable BE potentials on land currently not in agricultural production, using EU renewable energy sustainability criteria. Estimates for 2<sup>nd</sup> gen (woody and switchgrass), no irrigation/fertiliser. EU RED criteria include Biodiversity protection and maintenance of ecosystem function such as C storage.</td><td>53 EJ sustainably available on 167 mio ha of land with &gt;10t ha<sup>-1</sup> dm productivity, plus 33 EJ on 264 mio ha with 4-10 t ha<sup>-1</sup> dm productivity.<br>Fuss et al. cite other studies that arrive at ca. 100 EJ of sustainably useable BE.</td><td>97,98 99</td></tr><tr><td>ES, Biodiversity</td><td>Global</td><td>Biomass-balance model, explore range of scenarios.</td><td>2050: 26-141 EJ a<sup>-1</sup>, accounting for conservation area by c. 10-30% - 23 (18)-126 (98) EJ a-1.</td><td>100</td></tr><tr><td>ES, Biodiversity</td><td>Global</td><td>Modelling: PLUM land use change model (+ yields from LPJ-GUESS DGVM), 2050; vary parameters of the LUC model to explore uncertainty space<br>+ 9 EJ from 1st gen BE, explore if sufficient food calories for 2050 population can be achieved without (or very little) cropland expansion.</td><td>No combination of parameters in PLUM can achieve all three goals; a very few achieve it with only very little addnl. crop area; i.e. expansion of area for BE competes for other land area needs such as conservation.</td><td>101</td></tr><tr><td>ES, Biodiversity (area).</td><td>Global</td><td>LUC model (MagPIE), 300 EJ a<sup>-1</sup> in 2100 (upper end of 1.5/2deg trajectories).</td><td>Area needs for BE crop: 310 mio ha in 2050, ca. 640 mio ha in 2100, in BE-only case. Forest (and pasture) area</td><td>102</td></tr></table>
<table><tr><td></td><td></td><td>5 scenarios that each target one environmental aspect (reduce deforestation, improve N fertiliser efficiency, protect water, higher yields & combination).<br>2<sup>nd</sup> generation BE crops only.</td><td></td><td>declines, ca. 100 mio ha forest lost in BE vs. base-case.<br>No additional measure: BE demand in 2100 enhances LUC CO<sub>2</sub> emissions by (+437%), N losses (+34%), water use (+142%) food price (+28%) (compared to no BE case). Global cumm. LUC emissions due to BE-related LUC: 293 Gt CO<sub>2</sub> in 2100; they expect though that the C-debt to be offset by energy gain and CCS.<br>Single-sector protective measures alone also do not have much benefit: For instance, BE + avoided def. → lowers deforestation CO<sub>2</sub> emissions but increases food prices even more than in the BE case (also BE prices rise). Also N fertiliser efficiency: losses 165 Tg N a<sup>-1</sup> (2100) vs. 324 Tg in the BE case and 242 Tg in the base case.<br>Agricultural intensification (land sparing), assuming technological yield increases, lowers some negative side effects.<br>Overall a combination of measures achieves the best effect.<br>Impact of 67EJ a<sup>-1</sup> BE in 2030 is 146-57 = 89 GtCO<sub>2</sub> (57 is the value w-out BE), in 2050 (100 EJ a<sup>-1</sup>) +180% LUC emissions compared to no BE case</td></tr><tr><td>ES (climate)</td><td>Global</td><td>Compare DGVMs C cycle in response to a range of IAM LUC scenarios within RCP 1.9</td><td>DGVMs do not match the cumm. C uptake in the IAMs (Target IAM 130 Gt C in 2100).</td><td></td><td>103 104</td></tr></table>
<table><tr><td></td><td></td><td>And RCP 2.6 (IMAGE, MagPIE);<br/>2<sup>nd</sup> gen. in the IAMs not the DGVMs.</td><td>Total land C pool (veg, soil, geological) was found to be lowest using 1.5°C_IM. + RCP1.9.<br/>Depending on assumptions, carbon lost due to LUC for bioenergy offsets to large degree the C-savings in BECCS (e.g. legacy emissions from soil C).</td><td></td></tr><tr><td>ES (climate)</td><td>Global</td><td>Review/Meta-analysis of case studies of LUC for bioenergy</td><td>Impacts depend on conversion and alternative land uses: arable to short rotation coppice or perennial grass: enhance SOC (5-26%); grassland to SRC: more or less neutral for SOC; grassland to perennial grass and forest to SRC decrease SOC (ca. -11%). In forests, even residue use may be more beneficial if e.g. in long-lived products (rather than combusted) such as composite panels. Using logging residues can have negative impacts on soil or habitat for Biodiversity depending on whether the residues are left in the forest, or piled on site. Using wood for bioenergy need always be compared w. a case in which the forest were to remain untouched and continue to sequester C (counterfactual).</td><td>105, 106</td></tr><tr><td>ES</td><td>Global</td><td>Review no clear separation made between 1<sup>st</sup> and 2<sup>nd</sup> generation, implicitly most studies probably refer to 2<sup>nd</sup> gen. BE.</td><td>Sustainable BECCS for 2050 0.5 – 5 Gt CO<sub>2</sub> a<sup>-1</sup>; around 60 EJ a<sup>-1</sup> very conservative estimate when production is limited by protected area or deployed on degraded/marginal land. Higher estimates (by ca. 2050) related to studies where land expansion is allowed (ca. 130-270 EJ a<sup>-1</sup>). Values above 200 EJ have to rely either on very large area expansion or very large assumption on yield increases (for food and BE crops).<br/>3.3 GtC/a = ca. 170 EJ a<sup>-1</sup>; requires 380-700 mio ha land (in 2100), 25-46% of today's crop area; 720 km<sup>3</sup> a<sup>-1</sup> water (of which 260 km<sup>3</sup> a<sup>-1</sup> during production, mostly due to higher ET rates). Irrigation may up to double today's agricultural water withdrawals. Land area 2-4 times larger than identified as abandoned or marginal.</td><td>78, 82, 85</td></tr></table>
<table><tr><td></td><td></td><td></td><td>Sustainable technical potential up to 100 EJ a<sup>-1</sup> (high agreement), up to 300 (medium agreement). Traditional biomass use projected to decline to ca. 10-20 EJ a<sup>-1</sup>. Forestry-source BE estimates: 38-165 EJ a<sup>-1</sup>. Middle value of this range would require close to 300 mio ha. Forest residue has trade off since removes C from ecosystem that otherwise would decompose much more slowly. However, any biomass that would otherwise be burned without energy generation or decompose rapidly might as well be used to create energy. N2O, esp from fst. generation biofuels substantially reduced climate regulating effect. Likewise, C loses from land conversion due to BE (direct and indirect LUC) substantially reduce the GHG-saving. Climate side-effects arise also from C loss (direct and indirect LUC for BE) and albedo.</td><td></td></tr><tr><td>ES</td><td>Global</td><td>Modelling study; combine LUH1 with VISIT (C balance of LUC), SWAT (crop yields under diff. management). → BECCS from BE yields for different management options and for different CCS efficiencies. Compared to estimated yields necessary in the scenario (ie in the IAM).<br/>RCP 2.6, BECCS deployment of IAMs/RCP 2.6, LUH1, with 1<sup>st</sup> and 2<sup>nd</sup> gen BE. Total crop area increases from 1.6 to 2.1 bio ha in RCP2.6 (2100), of which is 83% from biofuel crops<br/>Two assumptions on 1<sup>st</sup> gen. BE N fertiliser and irrigation: baselines, fixed at ca. 2000 levels; high end: in all countries 160 kg N ha<sup>-1</sup> a<sup>-1</sup> by 2050 and irrigated area increases by 0.6% p.a. in each country.</td><td>15% to ca. 20% of necessary BE yields achieved by 2055 at low CO<sub>2</sub> capture (for baseline managementt. versus high-end inputs). At mid-level CO<sub>2</sub> capture, 36-45% of cumm. BECCS target by 2099.<br/>Yield increases for different crops types between 3 to 25 times today needed to meet BE target in scenarios, depending on crop and CCS efficiency.<br/>Even for high inputs (N, irrigation), crop yields of BE crops would have to increase up to two-fold across the 1<sup>st</sup> gen BE crops by 2050.<br/>Mean LUC C emissions 81 ± 34 Gt C by 2100. Higher than 61 Gt C computed in the original IMAGE RCP 2.6.<br/>Overall only max 36-59% of required BECCS can be achieved by 2055 w fst. and 2<sup>nd</sup> gen crops.</td><td>Before 2055 yield increases of 2-4 times for switchgrass or Miscanthus required to meet target, depending on CCS options/efficiency.<br/>Low CO<sub>2</sub> capture: 28-53% of cumm. BECCS target by 2099; Mid CO<sub>2</sub> capture: 40-77% of target BECCS by 2099.<br/>2<sup>nd</sup> gen BE crops might reach target but only if high inputs are used everywhere and if extremely optimistic CO<sub>2</sub> uptake efficiency is assumed (and without considering reduction of mitigation through N<sub>2</sub>O). Addnl. N fertiliser would be 40Tg N a<sup>-1</sup> by 2050 which is 37% of current global fertiliser consumption.</td><td>107</td></tr></table>
<table><tr><td></td><td></td><td>2<sup>nd</sup> gen: baseline &amp; high end 80kg N a<sup>-1</sup> scenario.<br>1st gen: liquid fuels; 2<sup>nd</sup> gen: biomass-gas conversion.</td><td></td><td></td></tr><tr><td>ES</td><td>Global</td><td>DGVM + IAM LUC scenarios, compare “standard” LUC w. scenario of maximising BECCS BECCS in 2100: +493 and 363 Mha compared to baseline LUC in the IAMs (SSP2, RCP 2.6)</td><td>BECCS reduces crop production (impact of CC and CO<sub>2</sub>, no specific technology increase), but less N loss (since more N is removed from harvesting the BE crops). Ca. 10% less compared no BE case</td><td>108</td></tr><tr><td>ES</td><td>Global</td><td>Dynamic forestry model + IAM (WITCH), explore woody bioenergy + CCS.</td><td></td><td>Given same price path, best sequestration outcome is combination of wood for sequestration and wood for bioenergy (CCS). Especially when prices increase (from ca. 125-230 $ onwards, CCS becomes economically feasible), relative contribution of forests for BE increase. In their RCP2.6 WBCCS explains half of total forest mitigation. From around 2080 onwards, natural forest area shrinks at the expense of WBCCS area.</td></tr><tr><td>ES (food)</td><td>Global + case studies</td><td>Review for present-day.<br>Review shows that there is unequivocal evidence for biofuel affecting food price negatively, which is also supported by economic theory; but magnitude highly uncertain.</td><td>2011-12 biofuel was 2.5 EJ a<sup>-1</sup>. Price multiplier: 8%/(EJ a<sup>-1</sup>) (biodiesel oilseeds), 23% (EJ a<sup>-1</sup>) (ethanol from grain), 38%/(EJ a<sup>-1</sup>) (biodiesel vegetable oil). 25% EJ a<sup>-1</sup>) (ethanol from switchgrass)<br>Price multiplier: % increase in commodity price per annual energy production (from this commodity)<br>Scenario studies seem to suggest that price multipliers decrease with the demand (even though the opposite would be expected). Studies seem to indicate 5-10%</td><td>109<br>83</td></tr></table>
<table><tr><td></td><td></td><td>of food price increase for 200-300 EJ a<sup>-1</sup>. (price multipliers well below 1%). But these studies include large yield increase both for food and bioenergy crops, AND large conversion of natural land into addnl. agricultural area. If that is not allowed, prices increases drastically</td><td></td></tr><tr><td>ES (carbon, food)</td><td>Global</td><td>Model, coupled economy (CGE)/atmosphere/ecosystem model. Biofuel crops implicitly 1<sup>st</sup> generation since using a crop-type parameterisation. 4 scenarios: no-policy (reference), energy-only (carbon pricing but not on land), no-biofuel (carbon pricing, including on land, no BE), energy+land (carbon pricing and BE)</td><td>With land carbon incentives, reforestation begins immediately and this creates a “safe” C-sink, since the carbon penalty of converting these forests to BE later is too high to compensate for the BE energy/carbon price gain. Food price: +22% in reference (5-fold increase in GDP); E-only: offset prices, i.e. benefits to agric. system from avoided env. damage but enhanced costs of land and of energy. No-biof and E+L: have highest food price (around double in E+L).</td><td>110</td></tr><tr><td>ES (food)</td><td>Global</td><td>Model (MagPie/Remind) Different scenarios of agricultural expansion, forest protection, yield increase.<br/>2<sup>nd</sup> gen. 100 EJ a<sup>-1</sup> in 2055, 300 EJ a<sup>-1</sup> in 2095 (Misc.). 70 and 250 EJ EJ a<sup>-1</sup> in scenario with avoided deforestation.</td><td>Compared to reference scenario (population growth, yield increase etc but no bioenergy demand), food price increases between 0 and 20%, depending on the region (in 2095). Has also large impact on water price several hundred (&gt;300) %).</td><td>80</td></tr><tr><td>ES (food)</td><td>Global</td><td>Model, several IAM Compare impacts of climate change in agric. sector with impacts of stringent CC mitigation. Use SSP1, 2,3.</td><td>By 2050, RCP 6.0 (climate impacts) less compared to scenario with mitigation and residual climate effects (RCP 2.6). Mitigation (including range of measures, not only bioenergy) increases food price by ca. 10 to 25% dep. on which SSP (2050 vs. 2010).</td><td>111</td></tr></table>
<table><tr><td>ES (food)</td><td>Global</td><td>Review</td><td>Of 17 global studies of bioenergy impacts on food security, 12 are negative (2 are neutral, 1 positive and 2 not useable for the review.</td><td>112</td></tr><tr><td>ES (food)</td><td>Global</td><td>Multi IAM study Baseline vs. RCP 2.6, across SSPs. GDP increases from ca. 50 trillion $ US to 1000 trillion $ US in SSP5 and ca. 350 trillion $ US (SSP4) Population (2100): 7.5 bio in SSP5, 9bio in SSP2, about 9 bio also in SSP4<br>2<sup>nd</sup> generation BE in mitigation scenarios; mitigation scenarios include bioenergy and AR, but mostly BE).</td><td>Increase (%) in food price about 50% of the increase in BE demand.</td><td>Change in food commodity price by 2100 (RCP 2.6): +110% (SSP2), +170% (SSP5), +570% (SSP4), compared to 2005; baseline: food price is more or less flat or declining, only SSP3 increase by 50%<br>Demand for dedicated energy crops: SSP2: ca. 10000 mio t dm/year (comp. to 4000 in baseline = +250%); SSP4 ca. mio 12000 t dm/year (comp. to 2500 in baseline = +480%); SSP5 >20000 mio t dm/year (comp. to more or less zero in baseline).</td><td>113</td></tr><tr><td>ES (food)</td><td>Global</td><td>Multi IAM study SSP1-SSP3, RCP 2.6 and 6.0.</td><td></td><td>Population at risk from hunger in all scenarios lower than today but compared to a ref. scenario, negative mitigation effects relatively much larger across all SSPs compare to climate effects.<br>Mitigation policies contribute more than half of the overall food commodity price increase.</td><td>114</td></tr></table>
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