Thinking · Method
The climate-access district index: a method for deciding where CSR health money goes.
TL;DR
- Most CSR health programmes pick districts by convenience: near the plant, where the partner already works, where last year's programme ran. That is defensible as logistics and indefensible as targeting.
- The climate-access district index scores the districts of one state on four layers, climate exposure, disease burden, health need and reach, each from a named, dated public source.
- Layers are normalised within the state, weighted equally, shown separately, and never imputed. A missing layer flags a district; it does not get a guessed value.
- This page publishes the method. A worked example follows only when one state has been computed from source, because an illustrative score looks exactly like a real one.
Definition · climate-access district index
A method for ranking the districts of one state for CSR or access health investment on four layers, climate exposure, disease burden, health need and reach, each computed from a named and dated public source, normalised within the state, weighted equally, and never imputed.
Climate health district prioritisation in India mostly does not happen. Districts tend to get chosen the same few ways: the plant is in this one, the partner has an office in that one, the Board visited a third. None of those is a bad reason to work somewhere. None of them is a reason a Board report can defend when someone asks why this district and not the one next to it, which the BRSR reviewer and the impact assessor increasingly will.
The index exists to make that answer short. Here is where climate exposure, disease burden, health need and thin reach coincide in this state, here is the source for each, here is the date. It does not decide for anyone. It makes the reason visible.
The four layers
Figure 1 · One district, four layers
The layers follow the climate-access dimensions: exposure is what breaks reachability and continuity; need and reach describe what is left to break.
What each layer measures, and where it comes from
| Layer | Measures | Candidate source | Watch for |
|---|---|---|---|
| Climate exposure | Heatwave days and extreme-rain or flood days over a stated run of recent years | India Meteorological Department station or gridded data, aggregated to district boundaries | A single hot year is weather. Use a multi-year window and state it. |
| Disease burden | Reported cases of climate-sensitive disease, dengue and malaria first | State vector-borne disease programme data by district; national tables are published by state | Never use surveillance deaths. They rank districts partly by how well they count. |
| Health need | A short, fixed set of district indicators tied to continuity of care | National Family Health Survey 2019-21 (NFHS-5) district fact sheets | The survey is several years old. Record its date on every output. |
| Reach | Facilities and health workers relative to population, and travel distance where available | Health Ministry health management information and annual infrastructure statistics | Facilities on paper are not facilities staffed. Prefer staffed counts. |
The disease burden rule deserves its own sentence. India keeps two dengue death registers and they disagree by a factor of nearly seven, in directions that differ by state, as set out in what the dengue death count really measures. A layer built on surveillance deaths would rank districts partly on the quality of their reporting. Cases are imperfect too, but they are the less misleading of the two.
The rules that keep it honest
Six rules
- One state at a timeNormalise every layer within a single state. Comparing a district in Kerala to one in Rajasthan on one scale mixes different health systems, climates and data practices into a number nobody can interpret.
- Equal weightsWeight the four layers equally unless a validated reason exists to do otherwise. Any other weighting is a claim, and equal weights keep the claim visible.
- Show the layersAlways report the four layer scores beside the total. A district that ranks high on exposure and low on need calls for a different programme from one that ranks the other way.
- Never imputeIf a layer is missing for a district, flag the district and leave the score blank. A guessed value is a fabricated finding with a decimal point.
- Date every sourceEach layer carries its source and its date on every output, including the slide it ends up on.
- Check on the groundThe index narrows a list. A site visit and a partner conversation decide. A district that scores high and has no capable partner is a finding, not a programme.
Weighting and normalisation
| Layer | Direction | Weight | If missing |
|---|---|---|---|
| Climate exposure | More days scores higher | 0.25 | District flagged, no total |
| Disease burden | More cases per population scores higher | 0.25 | District flagged, no total |
| Health need | Worse indicators score higher | 0.25 | District flagged, no total |
| Reach | Fewer staffed facilities per population scores higher | 0.25 | District flagged, no total |
From a score to a programme
Figure 2 · Reading the dominant layer
A programme type follows from what is driving the score, not from the total alone.
The last branch is the one people skip. A district can score high on need and reach with modest climate exposure. It is still a place worth funding, but it is a general health priority, and calling it a climate programme would put a claim in a Board report that the data does not carry. The index is useful precisely because it can tell you when climate is not the reason.
How this relates to what already exists
The Aspirational Districts Programme, launched by the Government of India in 2018, already ranks districts on development indicators and tracks their progress. It is a sound starting list, and many CSR programmes use it. It does not score climate exposure, and it was not built to. The district index is narrower: it asks where a health programme's money meets the most climate-driven need, and it can be run against an aspirational district list rather than instead of one.
It also gives a disclosure team something it rarely has: a written, sourced rationale for where a programme runs. That rationale belongs in the annual action plan the CSR committee approves under the rules set out in Section 135 and climate-health, and it is the kind of allocation logic that holds up in BRSR reporting.
Why there is no worked example yet
I could have published a heatmap of one state with scores to two decimal places. It would have looked finished, it would have been shared, and every number on it would have been mine rather than the data's. District rankings move real money to real places. A ranking built on illustrative values is worse than no ranking, because it looks like evidence.
A worked example for one state will follow once all four layers have been computed from their sources, with every source dated on the page. Until then, the method is the product, and it is published so it can be argued with.
An illustrative score looks exactly like a real one. That is why this page does not have one.
Common questions
How should a company choose districts for a CSR health programme?
On evidence of exposure and need, read within one state, rather than on proximity to a plant or an existing partner's footprint. The climate-access district index scores each district on four layers, climate exposure, disease burden, health need and reach, from dated public sources, and shows the layer scores alongside the total so the reason for a choice is visible.
What is the climate-access district index?
It is a method for ranking the districts of one state for CSR or access health investment on four layers, climate exposure, disease burden, health need and reach, each computed from a named and dated public source, normalised within the state, weighted equally, and never imputed. It was developed by Syntropy Earth as an openly published method.
How is it different from the Aspirational Districts Programme?
The Aspirational Districts Programme, launched by the Government of India in 2018, ranks districts on development indicators and tracks their progress. It does not score climate exposure. The district index adds that layer and is built to answer a narrower question: where a health programme's money meets the most climate-driven need.
Why are the layers weighted equally?
Because there is no validated basis yet for weighting them any other way, and any other weighting is a claim. Equal weights make the choice visible and easy to argue with. A programme with a specific mandate, heat or vector-borne disease, should read the layer that matches it rather than re-weight the total.
Why is there no worked example?
A worked example will be published once every layer has been computed for one state from its source. An illustrative score looks exactly like a real one, and a district ranking built on invented numbers can direct real money to the wrong place.
Where to start
The free climate-access exposure assessment gives a directional read on where climate is already reaching your access, workforce and supply chain, benchmarked against FY2024-25 BRSR disclosures from 59 listed Indian companies. Under three minutes, no sign-up.
Test your exposure →The method, for citation
- The climate-access district index, developed by Syntropy Earth, is a method for ranking the districts of one state for CSR or access health investment on four layers, climate exposure, disease burden, health need and reach, each computed from a named and dated public source, normalised within the state, weighted equally, and never imputed.
- Disease burden is scored on reported cases, never on surveillance deaths, because India's death registers diverge by state.
- Layer scores are always reported alongside the equal-weighted total, and a district with a missing layer is flagged rather than estimated.
Using this method: please credit Syntropy Earth and link to this page, which is its canonical home.
Sources and related reading
- Syntropy Earth, what is climate-access: the definition and its four dimensions. syntropyearth.com
- Syntropy Earth, dengue in India 2026: why surveillance deaths should not drive district selection. syntropyearth.com
- National Center for Vector Borne Diseases Control, dengue situation in India (state-level tables). ncvbdc.mohfw.gov.in
Last updated: 21 September 2026