\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,english]{babel}



\usepackage{float}








\begin{document}

\title{Expanding the E in ESG with high-resolution global mapping of
ecosystem services and corporate physical assets}



\author[1]{Lisa Mandle}%
\author[1]{Andrew Shea}%
\author[1]{Emily Soth}%
\author[1]{Stacie Wolny}%
\author[1]{Jeffrey R. Smith}%
\author[1]{Rebecca Chaplin-Kramer}%
\author[1]{Richard P. Sharp}%
\author[1]{Mayur Patel}%
\author[1]{Jesse A. Goldstein}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 27, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup








\sloppy


\textbf{Authors:} Lisa Mandle\textsuperscript{1}, Andrew
Shea\textsuperscript{2,3}, Emily Soth\textsuperscript{1}, Jesse A.
Goldstein\textsuperscript{1}, Stacie Wolny\textsuperscript{1}, Jeffrey
R. Smith\textsuperscript{4,5}, Rebecca
Chaplin-Kramer\textsuperscript{6,7}, Richard P.
Sharp\textsuperscript{6,8}, Mayur Patel\textsuperscript{1}

Affiliations

\begin{enumerate}
\tightlist
\item
  Natural Capital Project, Stanford University, Stanford, CA 94305 USA
\item
  Global Sustainable Finance, Morgan Stanley, New York, NY 10036
\item
  Current affiliation: Frontier \& Stripe Climate, Stripe, South San
  Francisco, CA 94080
\item
  Department of Ecology and Evolutionary Biology, Princeton University,
  Princeton New Jersey 08544
\item
  High Meadows Environmental Institute, Princeton University, Princeton,
  New Jersey 08544
\item
  Global Science, WWF. 131 Steuart St., San Francisco, CA 94105
\item
  Institute on the Environment, University of Minnesota, 1954 Buford
  Ave., St. Paul, MN 55108
\item
  SPRING, 5455 Shafter Ave., Oakland CA 94618
\end{enumerate}

\textbf{Abstract:}

Aligning economic activities with the global sustainable development
agenda requires understanding companies' impacts on nature. Here, we
present a new approach for quantifying the direct impacts of companies'
physical assets on nature based on global maps for eight ecosystem
services and biodiversity metrics. We apply this approach to a set of
over 2,000 global, publicly traded companies with 580,000 mapped
physical assets. We find that companies in utility, real estate,
materials, and financial sectors have the largest impacts on average,
but there is substantial variation among companies within all sectors.
In addition, we use high-resolution satellite imagery to assess the
impact of active lithium mines based on their footprints. We show that
the impact varies substantially among mines and can also be tracked
across time for a mine. This approach enables differentiation among
companies and assets based on their impacts to nature relative to their
revenue or production.

\textbf{Introduction}

Global acceleration of economic activity has raised many people's
standard of living but is also disrupting the climate system, driving
loss of biodiversity, and undermining Earth's life support
systems\textsuperscript{1}. These losses put global sustainable
development goals, and the long-term societal prosperity that they
underpin, at risk, as well as endanger key environmental agreements such
as the Paris Climate Accords\textsuperscript{2} and Kunming-Montreal
Global Biodiversity Framework\textsuperscript{3}. Consumers, investors,
and regulatory bodies are increasingly calling for greater transparency
around corporations' nature-related impacts to facilitate decisions that
are in line with sustainable economic growth and
development\textsuperscript{4,5}. Meaningful change requires accessible,
science-based information on corporate impacts to and dependencies on
nature.

Demand for quantitative information on nature-related risks and
opportunities is growing, driving important advances in measuring and
reporting on companies' impacts and dependencies on nature. Efforts such
as the Task Force on Nature-Related Financial Disclosures and the
Science-Based Targets Network are advancing frameworks and guidance for
tracking and reporting on nature-related impacts and
dependencies\textsuperscript{6,7}. Advances in methodologies, data, and
tools for measuring impacts to nature are making quantitative reporting
increasingly possible\textsuperscript{8--10}.

ESG (environmental, social, governance) approaches aim to evaluate
companies on a range of sustainability- and ethics-related issues. To
date, the focus of the environmental pillar of ESG has centered on
greenhouse gas emissions, air pollution, water and waste management,
and, more recently, impacts to biodiversity\textsuperscript{11,12}.
Existing approaches largely do not account for the ways corporate
activities impact or rely on ecosystem services. Ecosystem services are
the conditions or processes of ecosystems that help to generate benefits
to people\textsuperscript{13} including protection from natural hazards
such as coastal flooding, water purification for clean water for
drinking and recreation, and mental health benefits from enjoyment of
nature. A broader representation of the multiple facets of corporate
impacts on nature and its contributions to human well-being is critical
to effectively managing nature-related risks and opportunities and to
weighing tradeoffs between the benefits of economic activity and the
potential harms to people and economies from the loss of nature.

Here, we leverage recent advances in high-resolution, global ecosystem
service modeling\textsuperscript{14,15} and the growing accessibility of
high-resolution satellite imagery to develop a new, open-source approach
for quantifying the direct impacts of physical assets on ecosystem
services and biodiversity. Our approach scales from the level of
individual assets to collections of assets, such as corporations or
portfolios, using open-source, process-based ecosystem service
models\textsuperscript{14--16}. It overcomes limitations identified in
existing solutions for accounting for companies' environmental impacts,
including limited transparency in underlying models and
metrics\textsuperscript{17}, limited ability to differentiate impacts
within sectors\textsuperscript{18}, or reliance on regionalized values
with low spatial resolution\textsuperscript{18--20}.

We demonstrate how our approach provides new, decision-relevant insights
into corporate impacts on nature in two ways: First, we apply this
asset-based approach to a diverse set of global companies across all
sectors. Specifically, we evaluate the ecosystem services and
biodiversity impacts of over 2,000 companies included in the MSCI ACWI
index\textsuperscript{21} -- an equity index that spans large- and
mid-cap companies from developed and emerging market countries -- based
on over 580,000 mapped physical assets\textsuperscript{22} across all
continents except Antarctica. Second, we use high-resolution satellite
imagery to conduct a more detailed analysis of specific assets and show
the added granularity and context that can be gained with asset-specific
footprints. We focus on a set of active lithium mines with historical
production data\textsuperscript{22} because of the increasing demand for
lithium in an energy transition\textsuperscript{23,24}, the physical
nature of mining, and its environmental
impacts\textsuperscript{15,25,26}. We show how our approach can be used
to differentiate impacts among assets of similar activity types (e.g.,
among different mines), and to track impacts over time.

\textbf{Results:}

\textbf{Global maps of impacts}

To quantify the impacts of corporate physical assets, we first created
new global maps for each of 4 ecosystem services and 4 biodiversity
indicators under a scenario in which all land surface is returned to its
potential natural vegetation. The 4 metrics of impacts to ecosystem
services include: 1) reduced risk of flooding and erosion for coastal
populations (coastal risk reduction), 2) reduced erosion and sediment
trapping for improved water quality for downstream populations (sediment
retention); 3) retention of nitrogen pollution for improved water
quality for downstream populations (nitrogen retention); and 4) the
number of people within 1 hour travel time able to access nature for
potential recreation, cultural, and health benefits (nature access). The
4 biodiversity impact metrics include: 1) species richness of
amphibians, birds, mammals, and reptiles (species richness), 2) habitat
for threatened and endangered species (Red List species), 3) habitat for
endemic species (endemic species), and 4) Key Biodiversity Areas (KBAs).
See \emph{Methods} for more details on the calculation of each metric.
All metrics should be interpreted as indices with magnitudes that are
meaningful to compare within a metric but not between metrics. Given the
dominance of built infrastructure in the asset dataset (e.g., bank
branches, retail stores, cell towers, power plants; Table S1), we
assumed that the development of a physical asset resulted in the
complete loss of ecosystem service or biodiversity values as compared to
the retention of natural vegetation.

Our global analysis shows that ecosystem service levels vary greatly
within regions and among services, with 0.5\% of total land area
(\textasciitilde{}700,000 km\textsuperscript{2}, similar to the area of
Myanmar) falling in the 90\textsuperscript{th} percentile of values for
3 or more services (Figure 1a; Table S2). Coastal risk reduction
services were, by definition, restricted to coastal areas and greatest
in areas with large coastal populations. Areas of high sediment
retention value are very geographically dispersed but highest near
streams and rivers near populated areas. Nitrogen retention values are
concentrated in India, China, Europe, the Midwest United States and East
Africa, areas with high nitrogen loads from agriculture, high potential
for natural vegetation to purify water, and especially high human
populations with a need for clean water. Nature access is highest in and
around urban areas, with notable hotspots in Europe, Asia, and the
eastern United States, where high densities of people would benefit from
access to natural areas.

Biodiversity values, on the other hand, have greater levels of overlap
among the different metrics and a higher level of spatial concentration
than ecosystem services, with 3.2\% of total land area falling in the
90\textsuperscript{th} percentile for 3 or more biodiversity values
(Figure 1b; Table S2). Biodiversity values are greatest in the tropics,
and especially concentrated in the Amazon, Southeast Asia, the Guinean
Forests of West Africa and the Eastern Afromontane region of East
Africa, matching well-established hotspots of
biodiversity\textsuperscript{27}.

These patterns point to the importance of high-resolution data to
capture spatial variation in impacts to nature. The use of country or
regional averages -- an approach taken by some existing methods for
accounting for environmental impacts\textsuperscript{19,20,28} -- risks
substantially mis-classifying the degree of impact to ecosystem services
or biodiversity from an asset or company. This is especially the case
for ecosystem services, where values have high spatial heterogeneity,
varying greatly over small distances.

In addition, the distinctive patterns across ecosystem services
indicators as compared to biodiversity indicators (Fig. 1) shows that
biodiversity values are not a good proxy for ecosystem services, and
thus not sufficient for assessing nature-related impacts. Explicitly
incorporating ecosystem services into assessments of corporate
environmental impacts will be important to fully assess corporate
impacts and the ability to address the nature-related impacts of
companies on people.

\textbf{Variation in sector- and company-level impacts}

To quantify and compare company-level impacts, we used our global
ecosystem service and biodiversity maps to assess 585,569 physical
assets belonging to 2,173 companies in the MSCI ACWI index. In addition
to accounting for differences in impact due to asset locations, we also
accounted for differences in asset size by buffering asset locations by
the median asset size by activity type (see \emph{Methods/SI} for more
details). This advances beyond the point locations (latitude/longitude)
that are common to most corporate asset databases and allows us to
account for the fact that, for example, between a mine and a cell tower
in the same location, the mine will have more impact due to its greater
footprint on the landscape. We evaluate companies' total impacts for
each metric, as well as adjusted for total revenue. We omit nature
access as an ecosystem service metric in this analysis because it
primarily reflects how urban an asset is, which is not a relevant
measure of impact when considering existing assets from the wide variety
of sectors and companies evaluated here.

Companies in the utility, real estate, materials, and financial sectors
have the largest total impacts per company on average (Figure 2).
Utility companies tend to have high levels of impact across all
ecosystem service and biodiversity metrics, both per company and
relative to revenue. This is in part due to the large physical footprint
of utility companies. Companies in the financial and real estate sectors
have the highest impacts on coastal risk reduction. Their high
area-adjusted impact on coastal risk reduction indicates that their
assets tend to be in areas with high potential value for this service,
specifically in or near densely populated coastal areas.

The large range in variation in impact among companies within a sector
(Figure 2) points to the potential across sectors to identify companies
with relatively high or low levels of ecosystem services or biodiversity
impact. This allows decision makers to differentiate companies based on
their production or revenue efficiency with regards to ecosystem service
or biodiversity impacts. For example, someone looking to invest in a
particular sector could choose to invest in those companies with
relatively lower impact within that sector. Certain sectors and
industries may depend more directly on ecosystem services, such as
beverage companies within the consumer staples sector requiring reliable
sources of clean water, and companies within the materials sector
relying on timber for construction materials and paper products.
However, a company in any sector may have substantial ecosystem service
impacts. Therefore, these kinds of assessments should not be restricted
to certain sectors, especially as data and tools become more accessible.

Our approach can also provide more detailed resolution within a sector
at the industry level. For example, within the materials sector, one of
the highest-impact sectors, there is substantial variation among
industries on average but again high variation within industries (Figure
3). Metals and mining companies have the highest average impact, both in
absolute and revenue-adjusted terms, across all metrics except coastal
risk reduction. This is due to both the size and location of assets in
this industry. As the impact per km\textsuperscript{2} shows (Figure 3),
metals and mining company assets are located in areas with particularly
high values for red list species, overall species richness, and sediment
retention services. In contrast, companies in the chemicals and
containers and packaging industries have the lowest average impacts
(Figure 3). Companies in these industries have, on average, a smaller
footprint. Assets belonging to chemical companies tend to be in areas
with lower values for coastal risk reduction and sediment retention
impacts, as shown by the impact per km\textsuperscript{2} (Figure3).
Companies in both the chemicals and containers and packaging industries
tend not to have assets near Key Biodiversity Areas. Again, even within
a sector, our approach makes it possible to distinguish companies with
relatively low impacts, either within the sector overall or within an
industry. Taking these impacts into account during the investment
process can help mitigate risk or minimize impacts of investments.

\textbf{Evaluating asset-level impacts: mining}

This approach can also be used for more detailed evaluations of impacts
where assessments at the level of individual assets is desired and
feasible. As an example, we analyze individual lithium mining assets. We
integrated a dataset on the location of 23 global lithium mines with
high-resolution (\textasciitilde{}5m) satellite
imagery\textsuperscript{29} to map the footprint of individual mines. We
then combined these footprints with the global maps of ecosystem service
and biodiversity values to calculate mine impacts. These impacts account
for both mine location and the mine-specific area of land impacted.

Our results show that the magnitude of impact varies substantially among
mines (Figure 4). The range of potential impacts of lithium mining on
nature are determined by the location of Earth's lithium reserves
relative to the location of ecosystem service and biodiversity hotspots,
but even given this constraint, there are options for acquiring lithium
with higher or lower costs to ecosystem services and biodiversity. This
asset-specific information can be weighed alongside information such as
differences in productivity and operation costs. Understanding the
impacts of specific assets or project footprints on natural capital can
help project developers, corporates, infrastructure investors, and other
decision makers limit their negative impacts and optimize for production
efficiency when evaluating investments in new or existing development.

Using high-resolution, asset-specific footprints also reveals the
impacts of many lithium mines to be much higher than estimated by
assuming a fixed, median size (Figure 4a). The fixed approach is
generally able to differentiate between high- and low-impact mines for
the ecosystem services of sediment retention and nitrogen retention but
was not as effective at distinguishing differences for biodiversity
(Figure 4b). Assuming a median area by asset type may be both necessary
and sufficient when comparing across many assets, such as when comparing
across thousands of companies. However, where feasible, asset-specific
footprints provide valuable additional information and spatial precision
in assessing impact, especially when comparing among assets within an
activity type or analyzing an asset over time. Of the 23 lithium mines
evaluated, the mine with the largest footprint (Chaerhan Lake, China at
\textgreater{}3,250 km\textsuperscript{2}) is the most impactful mine on
endemic species, Red List species, and species richness, but other mines
had greater impacts on the other four measures. None impacted coastal
risk reduction. Five different mines represent the highest impact mines
across all seven metrics. Four of these five are in China, which
contains 8 of the 23 total lithium mines assessed.

Our approach can also leverage high resolution, high frequency satellite
imagery to assess how a mine's footprint -- and associated impact --
changes over time. Combining the mine footprint with high resolution
ecosystem service and biodiversity maps is important because these
impacts do not necessarily scale linearly with footprint area or
production. Using the Greenbushes, Australia lithium mine as an example,
with annual satellite imagery\textsuperscript{29} and production data
from 2016-2023\textsuperscript{30}, we find that the mine's total
footprint increased around 75 percent, while annual production increased
around 140 percent (Figure 5, Table S2). Impacts to nitrogen retention,
sediment retention, and species richness each increased over 80 percent,
slightly more than would be expected by the change in footprint size
alone, indicating the mine expanded into higher impact areas over time.
In contrast, impacts to nature access, habitat for endemic species, and
habitat for threatened and endangered species increased slightly less
than the increase in footprint size. Across all ecosystem service and
biodiversity measures, the impact per tonne of lithium produced
decreased between 2016 and 2023, apart from a period of low production
around 2021 likely due to COVID-19. Given that demand for lithium is
increasing and will likely continue to in a transition to a lower carbon
economy\textsuperscript{23,24}, intensity metrics can help show which
areas produce the most while limiting negative environmental impacts.

Capturing this level of granularity can help project developers and
investors explore the impact of an asset over time and analyze whether a
possible increase in negative impact due to a larger asset footprint is
justified by a meaningful increase in production. In addition to
evaluating past trends, the high-resolution ecosystem service and
biodiversity impact maps could also contribute to implementation of the
mitigation hierarchy at the asset level\textsuperscript{31}, enabling
project developers to avoid and minimize impacts to the most sensitive
locations. The ability to compare impacts among mines can help guide
extraction towards more sustainable options for this important resource.

\textbf{Discussion}

Our approach provides a science-based way to quantify the impacts of the
footprint of physical assets on multiple ecosystem service and
biodiversity metrics. This approach extends environment-related measures
for ESG with ecosystem service metrics. It is scalable from assets to
companies to portfolios, and across sectors, and thus can be used to
inform a range of policy, corporate, and investor decisions. It is able
to differentiate impacts based on assets' sizes and locations.

This approach accounts for the loss of ecosystem services and
biodiversity attributable to the loss of nature directly from physical
assets, similar to Scope 1 carbon emissions\textsuperscript{32}. For
some sectors, such as mining, these direct impacts may represent a
substantial portion of a company's impact on nature. For other sectors,
especially those with extensive supply chains, expanding the assessment
to account for supply chain impacts will be critical to capturing the
full extent of a company's impact. Here, there is great potential to
integrate our approach with methods from supply chain
analysis\textsuperscript{33} and spatial Life Cycle Assessment
(LCA)\textsuperscript{19}.

Further refinement of our approach could also allow for differentiation
in impacts among and within activity types. For example, an agricultural
field and a parking lot of equal size would have different impacts due
to differences in the maintenance of vegetation, soil permeability,
addition of fertilizers, and so forth. In addition, assets of the same
type and size may differ in their use of best management practices or
nature-based solutions, and thus differ in their impacts. Utilities and
energy companies, for example, are often stewards of large land areas,
and the management of ecosystems in and around solar facilities and
power transmission lines can lead to substantial variation in impact and
even the potential to create gains relative to current
conditions\textsuperscript{34,35}. These differences could be accounted
for within our approach by adjusting the ecosystem service or
biodiversity changes attributed to assets that employ certain
sustainable practices.

Finally, the sustainability field would benefit from the development of
common standards for defining baseline and impact scenarios so that
assessments could attribute impacts in a robust, intercomparable way.
Our approach currently uses potential natural vegetation as the baseline
conditions. Some urban areas have been developed for many decades, if
not centuries or millennia. Attributing full loss of nature at these
sites to the current asset owner could disincentivize activities in
existing urban areas and perversely incentivize new greenfield
development. An alternative approach could consider urban areas fixed
and focus on impacts outside historically developed
areas\textsuperscript{14}, use sites' restoration potential as a
reference scenario\textsuperscript{36}, or adjust impacts by the current
population density surrounding the site, although each approach would
come with its own methodological challenges and uncertainties.

We focus on accounting for impacts stemming from the loss of ecosystems,
filling an important gap in existing sustainability and ESG approaches.
Although accounting for pollution contributed directly by assets (e.g.,
fertilizer runoff from agriculture, mine tailings, the release of
chemicals or air pollution) is beyond the scope of the current analysis,
this would be a valuable future addition. Integrating estimates from
existing LCA approaches of pollution generated by
assets\textsuperscript{19,37} with the spatially explicit modeling of
impacts to people in our approach here could advance this aspect.

By using open-source models and drawing on the growing accessibility of
asset location data and high-resolution satellite imagery, we were able
to analyze the impacts of over 2,000 companies and nearly 600,000 assets
without relying on company-reported information on ecosystem service and
biodiversity impacts. This approach can provide corporate ESG metrics
focused on impacts to nature with greater transparency and the potential
for external verification. At the same time, data availability remains a
challenge: asset-level data is available primarily for publicly traded
companies, which represent only a fraction of corporate activities, and
even for these companies, data is incomplete\textsuperscript{38--40}.
Satellite imagery can capture or be used to infer many important
asset-level characteristics\textsuperscript{41,42} and is continuing to
drive advances in ecosystem service modeling at global and local
levels\textsuperscript{43}. Even so, these approaches cannot be expected
to fully capture all impacts or values, and on-the-ground information
will remain an important complement, especially for understanding local
values. Ultimately, further improvements to the accessibility,
completeness, and standardization of data will be important to extending
these approaches to meet demand from consumers, investors, regulators,
and companies themselves for high quality information on nature-related
impacts.

\textbf{Methods:}

\textbf{Global ecosystem service and biodiversity impact maps}

We developed high resolution (0.0028 degrees, \textasciitilde{}300
meters) global maps for quantifying assets' impacts on 8 measures of
ecosystem services and biodiversity. We first modeled potential natural
vegetation, or the vegetation that might exist absent development in a
given location, as the baseline condition. We followed the approach used
by Damania et al.\textsuperscript{14}, applying to all land areas
including current urban areas.

We then modeled the 8 measures of ecosystem services and biodiversity
under these baseline conditions. We selected four ecosystem services for
which global modeling was possible and which capture a range of values
of nature that are produced and delivered through unique phenomena
(e.g., hydrologic flows, coastal storms, human travel) such that they
are not spatially redundant with each other, and which are not commonly
captured by existing ESG approaches. These ecosystem services, sediment
retention, nitrogen retention, coastal risk reduction, and nature access
were modeled with the same methods as described in Chaplin-Kramer et
al.\textsuperscript{15}, using extensions of the
InVEST\textsuperscript{16} suite of models. For nitrogen retention, we
modeled loading rates based on the current land use/land
cover\textsuperscript{44}, representing fertilizer application in
agricultural areas and background loading rates for all other landcover
types. Combing nitrogen loading from a current scenario with retention
provided by a baseline scenario of potential natural vegetation allows
us to estimate the nitrogen retention service that would be provided by
returning a location to natural vegetation while keeping the rest of the
landscape under current conditions. Again following Chaplin-Kramer et
al.\textsuperscript{15}, for all ecosystem services biophysical measures
were combined with measures of the number of people benefitting to yield
a relative measure of realized ecosystem service value: number of people
downstream benefiting from clean water from nutrient and sediment
retention, number of people within the protective distance of coastal
habitats for coastal risk, and number of people within 1 hour travel of
natural and semi-natural lands for nature access.

We include four indicators of biodiversity, each capturing a different
dimension. Species richness is the number of different vertebrate
species (amphibians, birds, mammals, and reptiles) represented in an
area. Red List species is a measure of the number of threatened,
endangered, and critically endangered vertebrate species potentially
present at a location according to the IUCN Red List of
Species\textsuperscript{45}. Endemic species are represented by a
range-weighted species richness map, which weights rare species more
heavily than common species. This is done by weighting each species by
the inverse of its range size. Key Biodiversity Areas (KBAs) are areas
that contribute significantly to the global persistence of biodiversity,
identified according to a set of globally agreed-upon
criteria\textsuperscript{46}. Global maps of species richness, Red List
species, and endemic species were modeled under baseline conditions
(potential natural vegetation) following the methods in Damania et al.
2023\textsuperscript{14}. We created a global map with binary values,
indicating where locations were within 1 km of a
KBA\textsuperscript{47}. This was intended to account for the fact that
development activities can impact sensitive biodiversity and ecological
functions beyond a project's footprint, primarily within 1 km of
distance\textsuperscript{48,49}.

To identify hotspots and their areas of overlap, we calculated the
90\textsuperscript{th} percentile of values for each ecosystem service
or biodiversity metric. Oceans and areas with no data were excluded from
the percentile calculations. Areas with positive values for coastal risk
reduction and areas within 1 km of a KBA made up less than 10 percent of
land area, so all non-zero values for these metrics are included in the
hotspots.

\textbf{Quantifying company-level impacts}

A company's impacts on ecosystem services and biodiversity will depend
on its number of assets, the footprint size (area) of those assets, and
their location relative to ecosystem service and biodiversity values on
the landscape. Existing data on corporate assets may include an asset's
location in terms of latitude and longitude, but often does not include
information on the asset's footprint on the landscape. To estimate
impacts across thousands of companies with hundreds of thousands of
assets varying greatly in size, we estimated a median footprint size for
each asset facility category in the S\&P Physical Assets Database via
random selection, visual detection, and measurement using GIS. The
sampled assets' measurements were used to calculate median footprint
sizes (areas or widths) for each category. These median values were then
applied to each asset based on its category to create asset footprints.
(See the Supplemental Information for more details.)

Focusing on a suite of assets that generally result in paved or bare
ground and little vegetation (e.g., buildings, energy infrastructure,
mines), we assumed that development of a corporate asset results in the
complete loss of ecosystem services and biodiversity values estimated to
exist under baseline conditions. To calculate the impact of each asset
for each of the 8 ecosystem service and biodiversity metrics, we used
zonal statistics\textsuperscript{50} to calculate the total value under
the asset footprint for each of the previously described global impact
maps. We then summed asset-level impacts by company.

We applied this approach to the more than 2,000 companies within the
MSCI ACWI index, using the S\&P Physical Asset
database\textsuperscript{22} for information on each company's asset
locations and category. We grouped companies by their GICS (Global
Industry Classification Standard)\textsuperscript{51} sector and
industry to understand the differences among, and variation within,
sectors and industries.

\textbf{Estimating detailed asset-level impacts}

To quantify and compare impacts at the asset level within an asset's
facility category, we identified active lithium mines with associated
production data from the S\&P Metals and Mining
Dataset\textsuperscript{30}. This resulted in a set of 23 mines. High
resolution satellite imagery allowed us to map mine footprints and their
change over time annually from 2016-2023. We calculated mine-level
impacts for each year by summing the values of the previously described
global impact maps under the footprint.

\textbf{Acknowledgements:} We thank Harun Dogo for his pivotal role in
launching this collaboration, and Allison Bailey and Neil Nathan for
their feedback and contributions to identifying data sources. The Morgan
Stanley Institute for Sustainable Investing provided valuable input and
framing. This work was funded by the Morgan Stanley Institute for
Sustainable Investing. LM was also supported by a grant from the
Marianne and Marcus Wallenberg Foundation ''Advancing the Research
Frontier of Biosphere Stewardship, Strategic Research Collaboration and
Postdoc Program between The Stockholm Resilience Centre, Stockholm
University, and The Natural Capital Project, Stanford University.''

\textbf{Data and code availability:} We provide the global ecosystem
service layers under a CC BY 4.0 license at
https://github.com/natcap/natural-capital-footprint-impact, along with
code for quantifying impacts by asset type and company with
user-provided asset and company data. All other databases and datasets
used in this study derive from sources cited in the Methods section;
associated licenses prevent us from redistributing the derived datasets.

\textbf{References cited:}

1. IPBES. \emph{Summary for Policymakers of the Global Assessment Report
on Biodiversity and Ecosystem Services} .
https://zenodo.org/record/3553579 (2019) doi:10.5281/zenodo.3553579.

2. Conference of the Parties to the United Nations Framework Convention
on Climate Change. 7.d Paris Agreement. (2015).

3. Conference of the Parties to the Convention on Biological Diversity.
15/4. Kunming-Montreal Global Biodiversity Framework. (2022).

4. McKinsey \& Company \& Nielsen IQ. \emph{Consumers Care about
Sustainability---and Back It up with Their Wallets} .
https://www.google.com/url?q=https://www.mckinsey.com/industries/consumer-packaged-goods/our-insights/consumers-care-about-sustainability-and-back-it-up-with-their-wallets\&sa=D\&source=docs\&ust=1688750877223141\&usg=AOvVaw0pk3JVzaxmX0-bYMUdpuHR
(2023).

5. PwC. \emph{Asset and Wealth Management Revolution 2022: Exponential
Expectations for ESG} . www.pwc.com/awm-revolution-2022 (2022).

6. TNFD. \emph{The TNFD Nature-Related Risk and Opportunity Management
and Disclosure Framework Final Draft -- Beta v0.4} . (2023).

7. SBTN. \emph{Technical Guidance -- Step 1: Assess} . (2023).

8. ENCORE. \emph{ENCORE} https://encore.naturalcapital.finance/en.

9. WWF Biodiversity Risk Filter.
https://riskfilter.org/biodiversity/home.

10. GLOBIO - Global biodiversity model for policy support - homepage
\textbar{} Global biodiversity model for policy support.
https://www.globio.info/.

11. TFCD. \emph{Guidance on Metrics, Targets, and Transition Plans} .
(2021).

12. P\selectlanguage{ngerman}érez, L., Hunt, V., Samandari, H., Nuttall, R. \& Biniek, K. Does
ESG really matter---and why? \emph{The McKinsey Quarterly} (2022).

13. Guerry, A. D. \emph{et al.} Natural capital and ecosystem services
informing decisions: From promise to practice. \emph{Proceedings of the
National Academy of Sciences} \textbf{112} , 7348--7355 (2015).

14. Damania, R. \emph{et al.} \emph{Nature's Frontiers: Achieving
Sustainability, Efficiency, and Prosperity with Natural Capital} . (The
World Bank, 2023). doi:10.1596/978-1-4648-1923-0.

15. Chaplin-Kramer, R. \emph{et al.} Mapping the planet's critical
natural assets. \emph{Nat Ecol Evol} \textbf{7} , 51--61 (2023).

16. Natural Capital Project. InVEST. Stanford University, University of
Minnesota, Chinese Academy of Sciences, The Nature Conservancy, World
Wildlife Fund, Stockholm Resilience Centre and the Royal Swedish Academy
of Sciences (2023).

17. Berg, F., Kölbel, J. F. \& Rigobon, R. Aggregate Confusion: The
Divergence of ESG Ratings*. \emph{Review of Finance} \textbf{26} ,
1315--1344 (2022).

18. Moran, D., Giljum, S., Kanemoto, K. \& Godar, J. From Satellite to
Supply Chain: New Approaches Connect Earth Observation to Economic
Decisions. \emph{One Earth} \textbf{3} , 5--8 (2020).

19. Chaplin-Kramer, R. \emph{et al.} Life cycle assessment needs
predictive spatial modelling for biodiversity and ecosystem
services.\emph{Nat Commun} \textbf{8} , 15065 (2017).

20. Patouillard, L. \emph{et al.} Critical review and practical
recommendations to integrate the spatial dimension into life cycle
assessment. \emph{Journal of Cleaner Production} \textbf{177} , 398--412
(2018).

21. MSCI. MSCI ACWI Index (USD) Index Factsheet. (2023).

22. S\&P Global. S\&P Global Market Intelligence. (2022).

23. Hund, K., Porta, D. L., Fabregas, T. P., Laing, T. \& Drexhage,
J.\emph{Minerals for Climate Action: The Mineral Intensity of the Clean
Energy Transition} . (2020).

24. IEA. \emph{Net Zero by 2050 - A Roadmap for the Global Energy
Sector} . (2021).

25. Giljum, S. \emph{et al.} A pantropical assessment of deforestation
caused by industrial mining. \emph{Proc. Natl. Acad. Sci.
U.S.A.}\textbf{119} , e2118273119 (2022).

26. Luckeneder, S., Giljum, S., Schaffartzik, A., Maus, V. \& Tost, M.
Surge in global metal mining threatens vulnerable
ecosystems.\emph{Global Environmental Change} \textbf{69} , 102303
(2021).

27. Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A.
B. \& Kent, J. Biodiversity hotspots for conservation
priorities.\emph{Nature} \textbf{403} , 853--858 (2000).

28. International Energy Agency. \emph{Emission Factors: Database
Documentation (2022 Edition)} .
https://www.iea.org/data-and-statistics/data-product/emissions-factors-2022
(2022).

29. Planet Labs PBC. 2016-2023 Planet Labs PBC global quarterly
basemaps. (2023).

30. S\&P Global. S\&P Capital IQ Pro Metals and Mining. (2023).

31. Tallis, H., Kennedy, C. M., Ruckelshaus, M., Goldstein, J. \&
Kiesecker, J. M. Mitigation for one \& all: An integrated framework for
mitigation of development impacts on biodiversity and ecosystem
services. \emph{Environmental Impact Assessment Review} \textbf{55} ,
21--34 (2015).

32. World Business Councile for Sustainable Development \& World
Resources Institute. \emph{The Greenhouse Gas Protocol: A Corporate
Accounting and Reporting Standard, Revised Edition} . (2004).

33. Stadler, K. \emph{et al.} EXIOBASE 3. Zenodo
https://doi.org/10.5281/zenodo.5589597 (2021).

34. Walston, L. J. \emph{et al.} Modeling the ecosystem services of
native vegetation management practices at solar energy facilities in the
Midwestern United States. \emph{Ecosystem Services} \textbf{47} , 101227
(2021).

35. WWF \& BCG. \emph{Building a Nature-Positive Energy Transformation:
Why a Low-Carbon Economy Is Better for People and Nature} . (2023).

36. Brancalion, P. H. S. \emph{et al.} Global restoration opportunities
in tropical rainforest landscapes. \emph{Science Advances} \textbf{5} ,
eaav3223 (2019).

37. Farjana, S. H., Huda, N., Parvez Mahmud, M. A. \& Saidur, R. A
review on the impact of mining and mineral processing industries through
life cycle assessment. \emph{Journal of Cleaner Production}\textbf{231}
, 1200--1217 (2019).

38. Schlingemann, F. P. \& Stulz, R. M. Have exchange-listed firms
become less important for the economy? \emph{Journal of Financial
Economics} \textbf{143} , 927--958 (2022).

39. Murphy, A. America's Largest Private Companies 2022.
\emph{Forbes}https://www.forbes.com/lists/largest-private-companies/.

40. Maus, V. \& Werner, T. T. Impacts for half of the world's mining
areas are undocumented. \emph{Nature} \textbf{625} , 26--29 (2024).

41. Nassar, R. \emph{et al.} Quantifying CO2 Emissions From Individual
Power Plants From Space. \emph{Geophysical Research Letters}\textbf{44}
, 10,045-10,053 (2017).

42. Dethier, E. N. \emph{et al.} A global rise in alluvial mining
increases sediment load in tropical rivers. \emph{Nature} \textbf{620} ,
787--793 (2023).

43. Galaz García, C. \emph{et al.} The future of ecosystem assessments
is automation, collaboration, and artificial intelligence.\emph{Environ.
Res. Lett.} \textbf{18} , 011003 (2023).

44. European Space Agency. ESA CCI Land Cover. (2019).

45. IUCN. The IUCN Red List of Threatened Species. (2019).

46. IUCN. \emph{A Global Standard for the Identification of Key
Biodiversity Areas, Version 1.0} .
https://portals.iucn.org/library/node/46259 (2016).

47. BirldLife International. Digital boundaries of Key Biodiversity
Areas from the World Database of Key Biodiversity Areas. Developed by
the KBA Partnership: BirdLife International, International Union for the
Conservation of Nature, Amphibian Survival Alliance, Conservation
International, Critical Ecosystem Partnership Fund, Global Environment
Facility, Global Wildlife Conservation, NatureServe, Rainforest Trust,
Royal Society for the Protection of Birds, Wildlife Conservation Society
and World Wildlife Fund. (2019).

48. Chaplin-Kramer, R. \emph{et al.} Degradation in carbon stocks near
tropical forest edges. \emph{Nat Commun} \textbf{6} , 10158 (2015).

49. Haddad, N. M. \emph{et al.} Habitat fragmentation and its lasting
impact on Earth's ecosystems. \emph{Science Advances} \textbf{1} ,
e1500052 (2015).

50. Natural Capital Project. PyGeoprocessing. Stanford University,
University of Minnesota, Chinese Academy of Sciences, The Nature
Conservancy, World Wildlife Fund, Stockholm Resilience Centre and the
Royal Swedish Academy of Sciences. (2023).

51. MSCI. GICS® - Global Industry Classification Standard.
https://www.msci.com/our-solutions/indexes/gics.

\textbf{Figure 1.} \textbf{Global hotspots of impacts differ across
ecosystem service and biodiversity metrics.} The eight maps on the right
indicate the 10 percent of land area with the highest values for each of
four ecosystem service metrics (top) and four biodiversity metrics
(bottom). For coastal risk reduction and Key Biodiversity Areas, this
includes all areas with values greater than zero. These individual
metric maps were combined to create each of the two ecosystem service
and biodiversity hotspot maps on the left, showing which land areas fall
within the top 10 percent for one or more ecosystem service or
biodiversity metrics.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure} \textbf{2} . \textbf{Company-level ecosystem service and
biodiversity impacts vary across and within sectors.} Impacts are shown
for three ecosystem service metrics (coastal risk reduction, nitrogen
retention, sediment retention) and four biodiversity metrics (habitat
for endemic species, habitat for threatened and endangered species,
total species richness, and Key Biodiversity Areas). The top row shows
the total impact per company, which is affected by the number, size, and
location of each companies' physical assets. The middle row shows impact
adjusted by company revenue. The bottom row shows total impact adjusted
by the total area (km\textsuperscript{2}) of physical assets. A higher
impact per km\textsuperscript{2} indicates a company's assets are
located in areas with great ecosystem service or biodiversity values.
The values for each metric should be interpreted as indices; values are
comparable within a metric but not between different metrics. For each
boxplot, the midline indicates the median, with boxes extending from the
25th to 75th percentiles and whiskers extending to 1.5 times the
interquartile range. Outliers beyond 1.5 times the interquartile range
are indicated as dots. Note the log scale of y-axes.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllll@{}}
\toprule
Sector: & \textbf{Communica-tion Services} & \textbf{Consumer
Discretionary} & \textbf{Consumer Staples} & \textbf{Energy} &
\textbf{Financials} & \textbf{Health Care} & \textbf{Industrials} &
\textbf{Information Technology} & \textbf{Materials} & \textbf{Real
Estate} & \textbf{Utilities}\tabularnewline
No. companies & 113 & 234 & 169 & 70 & 338 & 199 & 334 & 294 & 216 & 115
& 91\tabularnewline
No. mapped assets & 9,449 & 69,160 & 31,519 & 17,205 & 205,497 & 11,999
& 11,004 & 6,279 & 9,049 & 194,028 & 20,380\tabularnewline
Footprint area (km\textsuperscript{2}) & 5.04E+07 & 2.67E+08 & 1.78E+08
& 6.83E+09 & 5.71E+09 & 7.13E+07 & 3.72E+08 & 5.49E+07 & 1.52E+09 &
8.45E+08 & 1.88E+10\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure 3.} \textbf{Within the materials sector, company impacts
vary across and within industries.} Impacts are shown for three
ecosystem service metrics (coastal risk reduction, nitrogen retention,
sediment retention) and four biodiversity metrics (habitat for endemic
species, habitat for threatened and endangered species, total species
richness, and Key Biodiversity Areas). The values for each metric should
be interpreted as indices; values are comparable within a metric but not
between different metrics. For each boxplot, the midline indicates the
median, with boxes extending from the 25th to 75th percentiles and
whiskers extending to 1.5 times the interquartile range. Outliers beyond
1.5 times the interquartile range are indicated as dots. Note the log
scale of y-axes. The top row shows the total impact, which is affected
by the number, size, and location of each companies' physical assets.
The middle row shows impact adjusted by company revenue. The bottom row
shows total impact adjusted by the total area (km\textsuperscript{2}) of
physical assets. A higher impact per km2 indicates a company's assets
are located in areas with great ecosystem service or biodiversity
values.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Industry: & \textbf{Chemicals} & \textbf{Construction Materials} &
\textbf{Containers \& Packaging} & \textbf{Metals \& Mining} &
\textbf{Paper \& Forest Products}\tabularnewline
No. companies & 102 & 16 & 13 & 75 & 10\tabularnewline
No. assets & 3,436 & 1,249 & 620 & 3,407 & 337\tabularnewline
Footprint area (km\textsuperscript{2}) & 1.09E+08 & 3.30E+07 & 1.73E+07
& 1.34E+09 & 1.42E+07\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure 4. Both mine size and location are important drivers of
variation in estimated impacts.} The estimated impact of 23 lithium
mines is based on high-resolution, mine-specific footprints is shown on
the y-axis, compared to the estimated impact using median mine area on
the x-axis. Panel a compares estimates of total impact, while panel b
compares ranks. A rank of 1 corresponds to the greatest impact for a
given metric. The 1:1 line is shown in black. Using the median mine area
substantially underestimates the impacts of the highest impact mines as
compared to using a mine specific-footprint. Comparing ranked impacts
(b), the median area-based approach differentiates higher and lower
impact mines for nitrogen retention, sediment retention and nature
access, but not for endemic species, Red List species, or species
richness. No mines were located in coastal areas or near Key
Biodiversity Areas, so coastal risk reduction and KBA metrics were
omitted.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

a)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

b)

\textbf{Figure 5. Patterns of change in mine impact vary over time and
among ecosystem service and biodiversity metrics.} Panel a shows the
footprint of the Greenbushes, Australia lithium mine mapped from
satellite imagery across three time periods (2016, 2019, and 2023).
Panel b shows the change in mine impact over time relative to 2016 in
terms of total impact, impact per tonne lithium produced, and impact per
km2 of mine footprint. Values for impacts to coastal risk reduction and
Key Biodiversity Areas (KBAs) are zero across all years, and so these
metrics are not shown.

a)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

b)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

\textbf{Supplemental Information:}

\textbf{Quantifying company-level impacts -- calculation of median
footprint sizes by facility category:}

We estimated a median footprint size for each asset facility category in
the S\&P Physical Assets Database. For each category, asset locations
were randomly selected for measurement in GIS. If a selected asset's
presence was visually detected and verified with high-resolution
satellite imagery, its physical footprint was traced and measured. For
all but two facility categories, areas were measured. The two remaining
categories (``transmission line'' and ``natural gas pipeline'') were
linear, thus their assets' widths were measured instead. Between two and
46 assets were sampled for each facility category. The sampled assets'
measurements were used to calculate median footprint sizes (areas or
widths) for each category.

For non-linear assets, the point location or each asset was buffered to
create a circle of the appropriate area for its category.

For the linear assets, a minimum spanning tree approach was used to
connect multiple point locations by unique asset number. Then, the
calculated median width was applied to create footprints.

\textbf{Table S1.} Number of assets per S\&P Physical Assets Database
facility category in analysis. Assets within facility categories may
fall under multiple sectors, as sectors are assigned the company level
based on a company's principal business activity.\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Facility category} & \textbf{Assets}\tabularnewline
Bank Branch & 183,116\tabularnewline
Cell Tower & 176,298\tabularnewline
Retail Store & 98,423\tabularnewline
Instn Address & 23,550\tabularnewline
Company Headquarters & 20,908\tabularnewline
Regulated Sites & 17,779\tabularnewline
Gas Receipt/Delivery Points & 16,901\tabularnewline
Power Plant & 14,044\tabularnewline
Transmission Line & 6,608\tabularnewline
Multifamily & 3,295\tabularnewline
Multi-Use & 2,803\tabularnewline
Mining Property & 2,370\tabularnewline
Data Center & 2,332\tabularnewline
Office & 1,951\tabularnewline
Warehouse/ Distribution Center & 1,809\tabularnewline
Shopping Center & 1,682\tabularnewline
Assisted Living & 1,468\tabularnewline
Residential & 1,415\tabularnewline
Self-Storage & 1,350\tabularnewline
Medical Office & 1,127\tabularnewline
Manufactured Home & 1,036\tabularnewline
Other Retail & 846\tabularnewline
Regional Mall & 819\tabularnewline
Industrial & 645\tabularnewline
Natural Gas Pipeline & 535\tabularnewline
Full-Service Hotel & 409\tabularnewline
R\&D Facility & 358\tabularnewline
Single Tenant & 334\tabularnewline
Health Care & 324\tabularnewline
Skilled Nursing & 302\tabularnewline
Single Family & 292\tabularnewline
Coal Mine & 279\tabularnewline
Independent Living & 232\tabularnewline
Inpatient & 230\tabularnewline
Gas Storage Facility & 218\tabularnewline
Limited-Service Hotel & 213\tabularnewline
Specialty & 205\tabularnewline
Hotel & 194\tabularnewline
Budget Hotel & 176\tabularnewline
Outlet Center & 170\tabularnewline
Land & 120\tabularnewline
Office Parks & 107\tabularnewline
Power Center & 81\tabularnewline
TV & 69\tabularnewline
Restaurant & 69\tabularnewline
Casino & 61\tabularnewline
Manufacturing & 59\tabularnewline
Student Housing & 50\tabularnewline
Rehabilitation & 48\tabularnewline
Parking Facility & 47\tabularnewline
LNG Facility & 38\tabularnewline
Dockside Casino & 31\tabularnewline
Industrial Park & 29\tabularnewline
Continuing Care Retirement & 26\tabularnewline
Recreation & 22\tabularnewline
Outpatient & 22\tabularnewline
Timber & 18\tabularnewline
Extended Stay Hotel & 18\tabularnewline
Cold Storage Unit & 16\tabularnewline
Track-Affiliated Casino & 12\tabularnewline
Golf Course & 10\tabularnewline
Flex/ Service Center & 10\tabularnewline
Cineplex Theater & 5\tabularnewline
Health Club & 4\tabularnewline
Psychiatric & 3\tabularnewline
AM & 3\tabularnewline
FM & 2\tabularnewline
Cruise Ship Casino & 2\tabularnewline
Car Dealership & 1\tabularnewline
Bowling Alley & 1\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table S2. The global area covered by the ecosystem service and
biodiversity hotspots shown in Figure 1,} given as both total coverage
area and percent of the total land area included in the analysis.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Number of ecosystem service hotspots & Hotspot area
(km\textsuperscript{2}) & Percent of total land area\tabularnewline
\midrule
\endhead
1 & 18,684,090 & 13.9\tabularnewline
2 & 6,971,635 & 5.2\tabularnewline
3 & 694,236 & 0.5\tabularnewline
4 & 27 & 0.00002\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Number of biodiversity hotspots & Hotspot area (km\textsuperscript{2}) &
Percent of total land area\tabularnewline
\midrule
\endhead
1 & 22,281,812 & 16.5\tabularnewline
2 & 8,434,036 & 6.3\tabularnewline
3 & 3,828,870 & 2.8\tabularnewline
4 & 534,993 & 0.4\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table S3.}\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Metric & \% absolute change & \% change intensity\tabularnewline
footprint area & +77.4 & -26.4\tabularnewline
production & +141.0 & NA\tabularnewline
nitrogen retention & +80.5 & -25.1\tabularnewline
sediment retention & +82.9 & -24.1\tabularnewline
nature access & +76.1 & -26.9\tabularnewline
endemic species & +71.8 & -28.7\tabularnewline
Red List species & +75.3 & -27.3\tabularnewline
species richness & +82.5 & -24.2\tabularnewline
\bottomrule
\end{longtable}

\selectlanguage{english}
\FloatBarrier
\end{document}

