1. 1
1. Scenario modelling
Value chain mapping
Market competition
3. Company exposure
Calculate individual company
exposure to shocks based on
value chain:
Model how counterparties are likely to
respond to these shocks based on
competitive dynamics of the market:
 Cost pass-through to consumer
 Market share adjustments
 Choice of production location
2. Transmission channels
Calculate shocks to agriculture and
food companies by commodity and
country:
 Cost shocks
 Demand shocks
To do so, combine scenario variables
(state of nature with production patterns)
with additional research on regulation.
Shocks are caused by the following state
of nature variables:
 Transition risk variables (impacts
on nature): i) Water pollution, ii)
Deforestation, iii) Protected areas, iv)
Air pollution.
 Physical risk variables
(dependencies on nature): i) Water
deficit, ii) Water quality,
iii) Soil quality, iv) Pollinator
population.
These variables also raise commodity
prices and hence, input costs for
downstream sectors
 Breakdown of sales by product
 Breakdown of sales by location
 Ultimate location of production (based
on modelled value chain relationships)
Scenario narratives
Scenario variables
Define future narratives of the
integrated nature and climate transition
across scenario set, such as:
 Protected area expansion
 Deforestation regulation
 Diet & consumption shifts
 Water pollution regulation
Assess production patterns consistent
with these outcomes such as total land
use, location and yields for different
commodities in different regions.
 Net present value of
changes to future flow
of profits
 Change in
security/equity value
 Credit risk
Quantified
financial risks
Risk outputs
Project state of nature variables
such as deforestation, water
stress, and water pollution.
Intermediate output Link to Locate Link to Evaluate Link to Assess
The financial impact of nature-related risks can be quantified at the
company level using three modelling steps
 Nature impact footprint (deforestation,
pollution, water withdrawal)
Quantified
impacts on nature
Linkages to TNFD risk assessment approach

2. Parameterization a transition scenario involves close examination
of policy and technology trends to determine possible futures
2030 2050
2020
FPS + Nature parameters
Note: All values shown here are at the global level
Emissions pricing and regulation2
USD/tCO2 in the land use sector, implicit3
Bioenergy
EJ production of second-generation bioenergy
Diet shifts
Ruminant meat production (Mt DM/yr)
Deforestation and afforestation
Forest land (Mha)
Food waste
% of food wasted
Nature markets
USD/ha/yr for a biodiversity credit
Land protection5
% global terrestrial protected surface area
Land restoration
% global terrestrial surface area under restoration6
Sustainable agriculture
Nitrogen uptake efficiency (%)4
<1
8
38
4,000
26
<1
15
0
56
54
17
40
4,100
24
12
20
4
60
105
90
37
4,300
20
45
24
6
65
Climate
Overlapping climate and nature
Nature
Update: Diet shifts are adjusted to
better account for regional variation,
consumer responses to prices, and
slower-than-initially-anticipated
alternative protein market growth
Update: Sustainable agriculture levers
account for emerging policy ambition to
improve nitrogen fertiliser use efficiency
while food waste reduction ambition
increases
Addition: New modelling levers are
added to account for nature-related
policy action
1. Updated levers are aligned with the most recent release of FPS (FPS 2022 – see Appendix) 2. Weighted average of modelled implicit carbon price 3. Implicit carbon prices proxy for a range of policies/regulations targeting a reduction in land use emissions 4. Average
across regions 5. FPS 2022 accounts for current protected areas and protection of biodiversity hotspots only, after 2025 and limited to a subset of countries 6. Additional restored terrestrial land compared to 2020 (intentional restoration only, occurring due to human
intervention)

3. 3
Changes in value due to nature risk exposure vary significantly
across subsectors
<1%
1.2%
0.2%
<1%
0.8%
1.2%
Portfolio Average
3.4%
Cumulative NPV profit impact by sub-sector
Unmigrated % change in NPV relative to baseline, GBF-Aligned Scenario, 2020-30
0
-10
50
10
30
20
40
Palm
oil
32%
10%
Beef
Dairy
Fruits,
veg.,
nuts
Corn
28%
Soybean
Cotton
47%
22%
26%
22%
Subsectors with estimated net profit losses relative to baseline
Subsectors with net profit
gain relative to baseline
Upstream Mid & Downstream
The dairy alternatives
subsector may gain
significantly from the
modelled nature
transition. This is driven
principally by diet shifts
towards alternative
proteins
2%
Agricultural
Support
Providers
Seafood
Processors
Pet
Food
Manufacturing
Food
and
Beverage
Retail
Dairy
Products
Production
General
Food
&
Bev
Products
6%
Fertilizers
Manufacturing
2%
Meat
and
Seafood
Processing
General
Food
Production
3%
Restaurants
and
Bars
Poultry
Products
Processors
1%
4%
Canned
Food
Production
Other
Food
Production
Other
Food
and
Bev
Services
2%
Sweets
and
Snacks
Production
Non-Alcoholic
Bev
Production
Other
Services
Alcoholic
Beverage
Production
Chemical
Flavor
and
Fragrance
Tobacco
Product
Manufacturing
Agrochemicals
Manufacturing
Nutritional
Health
Pharmaceuticals
5%
14%
1%
8% 5% 5% 4% 4% 4% 3% 3% 3% 1%
2% 1%
Impacts (transition risk)
Supply chain cost impact
Dependencies (physical risk)
Demand shift (transition risk) Regulatory impact (transition risk)
Reputational impact (transition risk)
1% -5%
Ag Mach.
Manufac.
0%
Dairy Alt.
Manufac.
191%
Example Output - Translate drivers of risk into financial value at stake
Subsector value impact across MSCI World Index agriculture, food and beverage companies

4. 4
Variation in company impacts can differ markedly within the same
subsector, underscoring the need for company-specific analysis
X%:Y%
Impact range
1. A company may have revenue streams across multiple business units, hence there are more observations shown here than agricultural, food and beverage companies in the index.
2. PMP stands for products manufacturing and processing.
3. Filtered for outliers. Unmitigated losses greater than 50% and meat alternative profit increases are not shown here.
4. Bubble size is determined by weighted mkt cap in each company business unit, but it is not directly proportional. Each company is assigned to a decile between 1-10 to ensure visibility for smaller
companies.
mkt cap4
-50% -15%
-30% 5%
-25% 10%
-45% -40% -35% -20% -10% -5% 0% 15%
Other Food & Bev PMP
Ag. Commodities Production
Meat/Dairy Alternatives PMP
Livestock and Dairy
Retail, restaurants & bars
Livestock and Dairy PMP
Agricultural inputs
Other
Unmitigated change in NPV by company business unit123, % change relative to baseline, GBF-Aligned Scenario, 2020-2030
-231%:0%
-48%:-25%
-15%:0%
-9%:-3%
+19:+185%
-28%:-2%
-34%:+1%
-6%:0%
Additional
impacts
Additional
impacts
Additional
impacts
Preliminary results
Company-level value impact for the MSCI World Index agriculture, food and beverage companies

5. 5
Understanding options for response can help make the case for
early action to mitigate risk exposure
5%
-20%
-25% 0%
-15%
-30% -10% -5% 10%
Restaurants and
food service
Agricultural inputs
Food retailers
Agricultural products/
commodities
Animal proteins
Food and beverage
manufacturers/processors
NB: Company-sector averages differ from sector averages shown previously because companies often derive revenue from sources beyond their sector of classification.
Company result Company-sector average
Company result Company-sector average
Unmitigated
With response
Estimated change in
NPV from 2020-2030,
by company, % change
-26%:+4%
X%:Y%
Impact range
-22%:+1%
-7%:+6%
-6%:+2%
-12%:+0%
-7%:+0%