Appendix B: Data, Tools, and Open Infrastructure

1 Purpose of this appendix

Earth–economy thinking is only as powerful as the data and tools that support it.

This appendix introduces:

  • the kinds of data Earth–economy models rely on,
  • the tools used to work with them,
  • and why openness is not a philosophical preference but a technical necessity.

You do not need to master these tools to understand the book.

But you should know:

What an Earth–economy model is made of.

2 The three data pillars

Integrated models rest on three broad classes of data.

2.1 1) Economic structure

These describe how economies are wired:

  • input–output tables,
  • social accounting matrices,
  • sectoral production data,
  • trade flows,
  • household income and expenditure.

They encode:

  • who buys from whom,
  • who earns income,
  • and how sectors interlock.

In Earth–economy models, these tables become:

  • the skeleton of the economic system.

2.2 2) Biophysical systems

These describe the Earth:

  • land cover and land use,
  • soils and productivity,
  • climate and weather,
  • hydrology,
  • carbon stocks,
  • species and habitat.

They are often:

  • spatial,
  • gridded,
  • and dynamic.

They encode:

  • where nature is,
  • what it does,
  • and how it changes.

2.3 3) Human outcomes

These describe what ultimately matters:

  • health,
  • food security,
  • risk exposure,
  • access to services,
  • livelihoods.

They connect:

  • ecological change
    to
  • human well-being.

Without this layer, models become technocratic.

3 Why integration is hard

Each data pillar lives in a different world:

Domain Typical scale Typical unit Typical format
Economics National / sector Dollars, jobs Tables
Ecology Local / spatial Hectares, tons Rasters, vectors
Human welfare Household / region People, outcomes Surveys, indicators

Earth–economy modeling exists to translate between these worlds.

This requires:

  • spatial matching,
  • unit conversion,
  • temporal alignment,
  • and conceptual mapping.

It is not glamorous work.

It is the work that makes everything else possible.

4 The open-source stack

Modern Earth–economy modeling is built on:

  • open programming languages (Python, R, Julia),
  • open geospatial libraries,
  • open optimization tools,
  • open data formats,
  • open repositories.

Why openness matters:

  1. Reproducibility
    Others can verify results.

  2. Extensibility
    New regions, sectors, and services can be added.

  3. Trust
    Assumptions are visible.

  4. Education
    Students can learn from real systems.

Closed models create:

  • dependence,
  • opacity,
  • and fragility.

Earth–economy modeling as public infrastructure requires openness.

5 What a workflow looks like

A simplified pipeline:

  1. Ingest:
    • economic tables,
    • land-cover maps,
    • climate data.
  2. Harmonize:
    • align regions,
    • align sectors,
    • align units.
  3. Simulate:
    • apply policy rules,
    • compute prices and quantities,
    • update stocks.
  4. Translate:
    • land change → ecosystem services,
    • services → productivity and risk.
  5. Evaluate:
    • GDP,
    • emissions,
    • biodiversity,
    • inclusive wealth.
  6. Visualize:
    • maps,
    • trajectories,
    • tradeoffs.

Every step is data work.

6 Tools as intellectual scaffolding

Tools are not neutral.

They shape:

  • what can be asked,
  • what can be seen,
  • and who can participate.

A spreadsheet invites:

  • static thinking.

A spatial model invites:

  • place-based reasoning.

A dynamic system invites:

  • path thinking.

Earth–economy tools are designed to make:

  • feedback,
  • stocks,
  • and futures legible.

7 The Doughnut in data

The Doughnut becomes operational when:

  • social floors are measured,
  • ecological ceilings are quantified,
  • trajectories are simulated.

That requires:

  • indicators,
  • thresholds,
  • and models that connect them.

Without data, the Doughnut is a picture.

With data, it becomes a constraint on futures.

8 Exercises

  1. Data mapping.
    Choose one sustainability problem (e.g., deforestation).
    List:

    • one economic dataset,
    • one ecological dataset,
    • one human-outcome dataset
      that would be needed to model it.

  2. Tool reflection.
    Describe a tool you already use (Excel, GIS, Python, R).
    What kinds of questions does it make easy?
    What kinds does it make hard?

  3. Open science.
    In one paragraph, explain why closed models are risky in public decision-making.

This appendix completes the infrastructure layer of the book.

You have seen:

  • how systems are conceptualized,
  • how they are formalized,
  • and now how they are built.

Earth–economy modeling is not magic.

It is:

  • data,
  • rules,
  • and care.

At planetary scale.