Analysis¶

This section describes post-hoc analyses that can be performed on solved models to extract insights about production, consumption, and the environmental and health impacts of food systems.

Statistics Extraction¶

The statistics extraction produces standardized CSV files summarizing key model outputs. These files provide a consistent interface for downstream analysis and visualization, extracting data from the solved PyPSA network using actual dispatch flows rather than capacity-based estimates.

Running the Extraction¶

# Extract all statistics for a scenario
tools/smk -j4 --configfile config/<name>.yaml -- \
    results/{name}/analysis/scen-default/crop_production.csv

# Or request any downstream plot to trigger extraction automatically

Output Files¶

All statistics are written to results/{name}/analysis/scen-{scenario}/.

crop_production.csv — Crop production by crop, region, and country

Column	Type	Unit	Description
`crop`	string	—	Crop identifier (e.g., `wheat`, `maize`, `grassland`)
`region`	string	—	Production region identifier
`country`	string	—	ISO 3166-1 alpha-3 country code
`production_mt`	float	Mt	Production quantity in megatonnes

Sources include single-crop production links, grassland production, and multicropping links (where multiple crops share the same land).

land_use.csv — Land allocation by crop, region, resource class, and water supply

Column	Type	Unit	Description
`crop`	string	—	Crop identifier
`region`	string	—	Production region identifier
`resource_class`	string	—	Land suitability class (e.g., `VS`, `S`, `MS`)
`water_supply`	string	—	Water regime (`rainfed` or `irrigated`)
`country`	string	—	ISO 3166-1 alpha-3 country code
`area_mha`	float	Mha	Cultivated area in million hectares

For multicropping systems, land area is attributed to individual crops proportionally by their yield (efficiency) on that land.

animal_production.csv — Livestock product output by product and country

Column	Type	Unit	Description
`product`	string	—	Product identifier (e.g., `dairy`, `meat-cattle`, `eggs`)
`country`	string	—	ISO 3166-1 alpha-3 country code
`production_mt`	float	Mt	Production quantity in megatonnes

food_consumption.csv — Food consumption and macronutrients by food and country

Column	Type	Unit	Description
`food`	string	—	Food identifier (e.g., `wheat`, `bread`, `beef`)
`country`	string	—	ISO 3166-1 alpha-3 country code
`consumption_mt`	float	Mt	Total consumption in megatonnes
`protein_mt`	float	Mt	Protein content in megatonnes
`carb_mt`	float	Mt	Carbohydrate content in megatonnes
`fat_mt`	float	Mt	Fat content in megatonnes
`cal_pj`	float	PJ	Energy content in petajoules
`consumption_g_per_person_day`	float	g/person/day	Per-capita daily consumption
`protein_g_per_person_day`	float	g/person/day	Per-capita daily protein intake
`carb_g_per_person_day`	float	g/person/day	Per-capita daily carbohydrate intake
`fat_g_per_person_day`	float	g/person/day	Per-capita daily fat intake
`cal_kcal_per_person_day`	float	kcal/person/day	Per-capita daily energy intake

food_group_consumption.csv — Consumption aggregated by food group and country

Has the same columns as food_consumption.csv, except with food_group instead of food. Food groups aggregate related foods (e.g., cereals, fruits, red_meat) for higher-level analysis.

Example Usage¶

Load statistics in Python for custom analysis:

import pandas as pd

# Load crop production
production = pd.read_csv("results/opt/analysis/scen-default/crop_production.csv")

# Total wheat production globally
wheat_total = production[production["crop"] == "wheat"]["production_mt"].sum()

# Load consumption with per-capita values
consumption = pd.read_csv("results/opt/analysis/scen-default/food_consumption.csv")

# Average per-capita protein intake
avg_protein = consumption["protein_g_per_person_day"].mean()

GHG Intensity¶

The GHG intensity analysis computes greenhouse gas emissions attributable to each unit of food consumed. This provides a consumption-centric view of impacts, tracing emissions through trade and processing networks back to production.

GHG intensity measures the greenhouse gas emissions per unit of food consumed (kg CO₂e per kg food). Unlike production-based accounting, this consumption-attributed metric traces emissions through the entire supply chain: if wheat is grown in one country, milled into flour, and consumed in another, the emissions from farming, processing, and transport are all attributed to the final consumption.

GHG Attribution Methodology¶

The GHG attribution uses a flow-based approach via sparse matrix algebra. The network of production, processing, and trade links forms a directed graph where each node (bus) receives material from upstream and passes it downstream. Emissions occur at production links (e.g., fertilizer N₂O, enteric CH₄).

The key insight is that emission intensity propagates through the network: the intensity at any bus equals its direct emissions plus the weighted average of upstream intensities. This gives a linear system:

\[\rho = e + M \rho\]

where \(\rho\) is the vector of emission intensities at each bus, \(e\) is the vector of direct emission contributions, and \(M\) is the weighted adjacency matrix (flow fractions). Solving \((I - M)\rho = e\) yields the consumption-attributed intensity at each food bus.

Running the GHG Extraction¶

# Extract consumption-attributed GHG intensity for a scenario
tools/smk -j4 --configfile config/<name>.yaml -- \
    results/{name}/analysis/scen-default/ghg_attribution.csv

Output files:

results/{name}/analysis/scen-{scenario}/ghg_attribution.csv

Per-country, per-food consumption-attributed GHG intensity including:

Column	Type	Unit	Description
`country`	string	—	ISO 3166-1 alpha-3 country code
`food`	string	—	Food identifier
`food_group`	string	—	Food group
`consumption_mt`	float	Mt	Consumption quantity
`ghg_kgco2e_per_kg`	float	kgCO2e/kg	GHG intensity
`ghg_usd_per_t`	float	USD/t	Monetized GHG damage

results/{name}/analysis/scen-{scenario}/ghg_attribution_totals.csv

Total consumption-attributed GHG emissions by country and food group:

Column	Type	Unit	Description
`country`	string	—	ISO 3166-1 alpha-3 country code
`food_group`	string	—	Food group
`ghg_mtco2eq`	float	MtCO2eq	Total emissions attributed to consumption

Net Emissions¶

The net emissions extraction reads the solved network’s emission aggregation links directly, providing the absolute net GHG balance including negative emissions from spared land sequestration.

# Extract net emissions for a scenario
tools/smk -j4 --configfile config/<name>.yaml -- \
    results/{name}/analysis/scen-default/net_emissions.csv

results/{name}/analysis/scen-{scenario}/net_emissions.csv

Net GHG emissions per gas and total:

Column	Type	Unit	Description
`gas`	string	—	Gas type (co2, ch4, n2o, total)
`net_mtco2eq`	float	MtCO2eq	Net emissions in CO2 equivalents

Health Impacts¶

The health impacts analysis computes marginal years of life lost (YLL) per unit of food consumed, based on dose-response curve derivatives at current population intake levels.

Health impact measures the years of life lost (YLL) per unit of food consumed. This is computed as the marginal effect—the derivative of the dose-response curve at current population intake levels. Foods with protective effects (fruits, vegetables, legumes) have negative values, while foods associated with health risks (processed meat, excess red meat) have positive values.

Health Attribution Methodology¶

Health impacts are computed by evaluating the slope of the piecewise-linear dose-response curves at current intake levels. For each (health cluster, risk factor) pair:

Current per-capita intake is computed from consumption flows and population
The slope of the log-relative-risk curve at this intake is determined
The chain rule converts this to YLL per unit intake change:

\[\frac{d(\text{YLL})}{d(\text{intake})} = \frac{\text{YLL}_\text{base}}{\text{RR}_\text{ref}} \cdot \text{RR} \cdot \frac{d(\log \text{RR})}{d(\text{intake})}\]
Units are converted from YLL per g/capita/day to YLL per Mt food

The result captures how marginal changes in consumption affect population health outcomes, accounting for where each country currently sits on the dose-response curve.

Running the Health Extraction¶

# Extract health marginals for a scenario
tools/smk -j4 --configfile config/<name>.yaml -- \
    results/{name}/analysis/scen-default/health_marginals.csv

Output files:

results/{name}/analysis/scen-{scenario}/health_marginals.csv

Per-country, per-food-group marginal health impacts including:

Column	Type	Unit	Description
`country`	string	—	ISO 3166-1 alpha-3 country code
`food_group`	string	—	Food group (risk factor)
`yll_per_mt`	float	YLL/Mt	Marginal years of life lost per megatonne
`health_usd_per_t`	float	USD/t	Monetized marginal health damage

results/{name}/analysis/scen-{scenario}/health_totals.csv

Total years of life lost by health cluster:

Column	Type	Unit	Description
`health_cluster`	int	—	Health cluster identifier
`yll_myll`	float	MYLL	Total years of life lost in millions

Sample Results¶

The following figures show consumption-weighted global averages of GHG intensity and health impacts by food group:

Bar chart showing GHG intensity by food group — Global average GHG intensity by food group (consumption-weighted). Animal products (red meat, dairy) show the highest emissions per kg, while plant-based foods generally have lower intensities.¶

Bar chart showing health impact by food group — Global average health impact by food group (consumption-weighted). Negative values indicate protective effects (fruits, vegetables, legumes, whole grains), while positive values indicate health risks. The magnitude reflects the marginal impact at current global intake levels.¶

Generating Global Average Plots¶

# Generate global average plots
tools/smk -j4 --configfile config/<name>.yaml -- \
    results/{name}/plots/scen-default/marginal_ghg_global.pdf \
    results/{name}/plots/scen-default/marginal_yll_global.pdf

Objective Breakdown¶

The objective breakdown analysis extracts the cost components that make up the model’s objective function, grouped into high-level categories. This enables analysis of how different cost drivers contribute to the total system cost.

Running the Objective Extraction¶

# Extract objective breakdown for a scenario
tools/smk -j4 --configfile config/<name>.yaml -- \
    results/{name}/analysis/scen-default/objective_breakdown.csv

Output file:

results/{name}/analysis/scen-{scenario}/objective_breakdown.csv

Single-row CSV with cost categories in billion USD:

Column	Type	Unit	Description
`crop_production`	float	bn USD	Land use and yield-related costs
`trade`	float	bn USD	Import/export costs
`fertilizer`	float	bn USD	Synthetic fertilizer costs
`processing`	float	bn USD	Food processing/conversion costs
`consumption`	float	bn USD	Consumption-related costs
`animal_production`	float	bn USD	Livestock production costs
`feed_conversion`	float	bn USD	Feed processing costs
`consumer_values`	float	bn USD	Utility from food consumption (negative)
`biomass_exports`	float	bn USD	Revenue from biomass exports (negative)
`biomass_routing`	float	bn USD	Internal biomass flow costs
`health_burden`	float	bn USD	Health costs from YLL
`ghg_cost`	float	bn USD	Emissions costs

The script validates that extracted categories sum to the model’s reported objective value and raises an error if they don’t match (within 1% tolerance). It also raises errors for unrecognized component patterns to ensure the analysis is updated when the model structure changes.