Program & theory of change
Program & Theory of Change: Turning Mission into a Fundable Clean-Energy Program
Before any proposal is written, funders want to see a clear logic: who benefits, what changes for them, and at what cost. We work with your leadership and field teams to translate mission language into a rigorous theory of change, a measurable results framework, and validated unit economics — the foundation every subsequent funding conversation will rest on.
Typical duration · 3-4 weeks
Samples generated 05 Sept 2026, 11:27 pm ISTWhat happens in this step
- 01Leadership and field-team workshop to unpack mission, context and current program assumptions
- 02Beneficiary segmentation — who is served, their constraints, and what 'change' means for each group
- 03Draft theory of change: inputs, activities, outputs, outcomes and impact statements
- 04Build a results framework with SMART indicators and verification sources
- 05Model unit economics across 2-3 program design options (capex/beneficiary, cost per outcome)
- 06Stress-test the framework against likely funder types (CSR, DFI, multilateral, philanthropic)
- 07Finalize a one-page logic model and narrative summary for internal and external use
What we need from you
- Current mission/strategy documents and any past program reports
- Existing M&E data, beneficiary surveys or field assessments
- Program budgets and historical cost data, if available
- Org chart and names of field staff to include in workshops
- Any prior funder feedback or rejected proposals
- Geographic and beneficiary scope you want to prioritize
Worked example (anonymised, illustrative)
Distributed Solar Irrigation Livelihoods Program · 8,000 smallholder households / ~3 MWp aggregate solar pump capacity · Eastern India, rural agrarian belt
An NGO running scattered solar-pump pilots needed a single fundable program design to approach CSR and blended-finance funders with a coherent story and defensible unit economics.
Sample deliverables from this step
Every sample below is analyst-written and anonymised for illustration — structure and depth mirror our real deliverables; figures and names are not from any client engagement.
Illustrative · Growthifye-preparedTheory of Change Report
Full logic model connecting inputs to long-term impact, with narrative justification for each causal link.
Sample excerpt · Logic Model Summary — illustrative figures
| Level | Statement | Indicator |
| Impact | Reduced diesel dependency & farmer income growth | % reduction in diesel spend |
| Outcome | 8,000 farmers shift to solar irrigation by Year 3 | # pumps commissioned |
| Output | Solar pump sets installed & O&M teams trained | # units installed |
| Activity | Site survey, vendor selection, installation | # sites surveyed |
| Input | Grant capital, technical partner, local NGO network | ₹ committed |
- Indicators are chosen to be verifiable with data your team can realistically collect
- Impact statements are deliberately conservative to withstand funder due diligence
Illustrative · Growthifye-preparedResults Framework (Logframe)
Indicator matrix with baselines, staged targets and verification methods, structured for both internal M&E and funder reporting.
Sample excerpt · Indicator Matrix (extract) — illustrative figures
| Indicator | Baseline | Year 1 Target | Year 3 Target | Verification |
| Households with solar pump access | 0 | 2,000 | 8,000 | Installation register |
| Diesel cost saved per household/year | ₹18,000 | ₹9,000 saved | ₹16,000 saved | Household survey |
| CO2e avoided (tCO2e/year) | 0 | 450 | 1,800 | Emission factor calculation |
| Women trained as pump operators | 0 | 150 | 600 | Training attendance logs |
- Framework is designed to be reused across CSR, DFI and multilateral applications without rework
- Verification sources are matched to your existing field capacity, not aspirational systems
Unit Economics Model
Editable cost model comparing program design options on capex, opex and cost-per-outcome basis, sensitivity-tested for funder scrutiny.
Sample excerpt · Cost-per-Outcome Sensitivity — illustrative figures
| Scenario | Capex/HH (₹) | Opex/HH/yr (₹) | Cost per tCO2e avoided (₹) | Payback (yrs) |
| Base case | 42,000 | 1,200 | 3,900 | 4.2 |
| Bulk procurement (-15% capex) | 35,700 | 1,200 | 3,300 | 3.6 |
| Extended O&M contract (+3 yrs) | 42,000 | 1,600 | 4,300 | 4.5 |
| Community co-financed (50% HH share) | 21,000 | 1,200 | 2,100 | 2.1 |
- Model is built in an open spreadsheet format so your finance team can update assumptions independently
- Used directly to justify budget lines in later proposal stages
Program Design Memo
Short decision memo comparing 3-4 program configurations against beneficiary reach, capex and likely funder fit, for board sign-off.
Sample excerpt · Program Options Compared — illustrative figures
| Option | Beneficiaries | Indicative Capex | Funder Fit |
| A: Solar pump grant-only | 8,000 HH | ₹34 Cr | CSR / philanthropic |
| B: Pump + micro-loan blend | 12,000 HH | ₹41 Cr | DFI / blended finance |
| C: Pump + agri-extension bundle | 6,000 HH | ₹39 Cr | Multilateral climate fund |
| D: Pay-as-you-own via cooperative | 10,000 HH | ₹37 Cr | Green bond / impact investor |
- Memo format is designed for a single board meeting decision, not extended deliberation
- Each option links back to the same theory of change to keep the mission narrative consistent
Outcomes
- A funder-ready program design with clear beneficiary logic and measurable outcomes
- Unit economics validated across 2-3 program scenarios before any capital is raised
- A single results framework reusable across grant, CSR and blended-finance applications
- Internal alignment between board, field teams and future funders on what success means
Questions clients ask
How is this different from just writing a grant proposal?
A theory of change is the reusable foundation — proposals for different funders are then written on top of it, rather than each being built from scratch with inconsistent logic.
Can you work with our existing program and M&E data?
Yes — we start from whatever survey, field or financial data you already have, and only recommend new data collection where it's essential for funder credibility.
What if our NGO runs programs across multiple clean-energy sub-sectors?
We can build parallel theories of change for each program line, then show funders how they roll up into one organizational impact narrative.
