This document explains how the fund measures the positive impact of its investments, aiming to make money while helping build a more sustainable and inclusive world. The fund favours companies whose core products tackle environmental or social problems, across areas like education, climate, healthcare and financial inclusion. It highlights Duolingo as an example: its language app and English test widen access to education. CATL is cited for large CO2 reductions, and Bank Rakyat Indonesia for providing financial services to 570 million people. No buy/sell moves or price changes are discussed—this is a methodology paper.
BG Positive Change Fund report outlines its impact investment analysis methodology: through a qualitative framework independent of investment analysis, it evaluates companies across three dimensions—products and services, business practices, and intent—and holds only companies whose core products or
This document is the fund's methodology paper rather than a monthly report, so it contains no monthly performance, performance attribution, or position change data; the following is organized according to the actual content.
In June 2026, BG Positive Change Fund published its Impact Measurement Methodology, explaining how it analyzes, reports, and accounts for the portfolio's positive impact. The fund explicitly sets out a dual objective: to achieve attractive investment returns and to help drive a more sustainable and inclusive world.
The document covers four areas: how to analyze a company's potential impact, how to report impact in the annual Positive Change Impact Report, and how to collect and calculate key impact data. The author states that the methodology is continuously evolving and will be informed by engagement with portfolio companies.
The portfolio only includes companies whose core products/services address global environmental or social challenges and improve the status quo. Impact analysis is independent of, but complementary to, investment analysis, and all aspects of a company's business are analyzed before investing.
| Component | What Is Assessed |
|---|---|
| Products and Services | Relationship between the product and the problem; breadth and depth of impact; importance of the product/service within the business and the context of the challenge |
| Business Practices | Value-chain actions and relationships with all stakeholders, used to judge whether sustainable growth can be achieved |
| Intent | Company mission and its implementation; strategy, actions, commitments, and structures; influence on the broader industry |
Duolingo is a portfolio holding. The document uses its full "positive chain" — Inputs → Activities → Outputs → Outcomes → Impacts — to illustrate the methodology, with data as of December 2025.
Thesis: Duolingo's core products are the language-learning app and website, which have expanded to children's literacy, music, math, and chess. Its Duolingo English Test (DET) is accepted by more than 6,100 educational programs worldwide, and English is the most popular language in 154 countries (mainly developing countries) — the product directly expands educational opportunity.
| Metric | Data |
|---|---|
| Estimated number of DET tests in 2025 | 600,000 tests |
| DET test takers using the test for higher education admission | 89% |
| Educational programs accepting DET | 6,100+ (global) |
| Language courses | 250+ |
| Monthly active users | 130 million+ |
| R&D investment (as of December 2025) | US$306.3m |
| Employees (as of December 2025) | 900+ |
| 2025 DET test-taker coverage | 219 countries/territories, 148 first languages |
| Teaching effectiveness | 2026 study: as effective as traditional classroom instruction for beginning language learners |
| A value in the document's illustration (metric not specified in the original) | 4.5 |
The document does not state any change in the direction of the Duolingo position; it only confirms "owned in the portfolio." It also provides no position weight or valuation multiple.
Impact is disclosed at two levels in the annual report: company-by-company (Positive Change hypothesis + positive chain) and portfolio-level summary (headline impact data, notable SDG contributions). Only impact derived from products/services is included in the Impact Report; business-practice impact is disclosed separately in the annual ESG and engagement report.
The positive chain is based on a Theory of Change, mapping change across five stages. Impact metrics are selected and monitored by impact analysts using public data; investment metrics (revenue growth, share price returns) come from third parties such as FactSet and Refinitiv.
| Stage | Definition |
|---|---|
| Inputs | Resources used by the company, such as financial capital and human capital |
| Activities | Use of inputs or other actions to produce outputs |
| Outputs | Products or services delivered to beneficiaries |
| Outcomes | Early/medium-term changes resulting from activities and outputs |
| Impacts | Long-term changes expected to occur as a result of the company's activities and outputs |
The author regards the Impact Report as the "foundation" of the Positive Change strategy and states the goal as being "as robust and transparent as possible," while acknowledging the structural difficulties of impact measurement. The reader should note that the entire document comes from the perspective of a position holder.
This section follows the preceding explanation of return and growth-rate calculations, then turns to the core of the entire document — the framework for collecting and aggregating 2025 impact data. The following continues in the same analytical style, focusing on methodological details not previously expanded, and supplements key data and deconstructed formulas.
The report sets out four principles for impact data collection that together constitute a "verifiability-first" conservatism:
Stacked together, these principles push the Headline Impact Data toward a "lower-bound estimate" in statistical terms. For example, if a company does not disclose its patient count, its contribution to the portfolio-level "patients treated" aggregate is zero. The advantage is high credibility, but during periods of uneven industry disclosure it can create significant understatement, especially among small and mid-sized companies or in markets without mandatory ESG disclosure.
The report lists company-level data under the four impact themes and provides aggregate results. The table below distills the key totals:
| Impact Theme | Representative Companies | 2025 Aggregate Impact | Unit / Metric Basis |
|---|---|---|---|
| Healthcare and quality of life | Sandoz, Dexcom, Vertex, Insulet | Over 1 billion patients | Patient treatment / disease-management encounters |
| Social inclusion and education | Coursera, Duolingo, Microsoft | 342 million registered learners | Registered learner count |
| Environment and resource needs | Schneider Electric, CATL, Rivian | Approximately 216 million tonnes CO₂e avoided | CO₂ equivalent (tonnes) |
| Base of the pyramid | Bank Rakyat Indonesia, MercadoLibre | 570 million people with access to financial services | Number of people covered by services |
Notable differences in metric definitions:
This mixture of heterogeneous metrics makes "Headline Impact" more like a dashboard than a single KPI. It cannot answer "which theme is more important," but it wins on transparency.
In addition, the environment theme contains values of vastly different magnitudes (e.g., Rivian's 417k, Prysmian's 1m, Schneider Electric's 183m), indicating that companies' calculation boundaries differ enormously — some may count only zero-emission vehicle models, while others include the energy-saving contribution of the entire product line. This adds noise to cross-company comparisons.
The report's proposed calculation path for the Impact Indicator is:
1. Company impact data → divided by company market capitalization (USD) → gives "impact density per US dollar of market capitalization";
2. Multiply by the company's portfolio weight within the fund;
3. Then multiply by the investor's actual investment amount.
Using the Bank Rakyat Indonesia example, this can be abstracted as:
\[
\text{attributable impact} = \frac{187,000,000 \text{ people}}{34,900,000,000 \text{ USD}} \times 1.98\% \times 1,000,000 \text{ USD}
\]
Mathematically, the result should be 106 people, not the 10,609 people given in the text. If "1.98%" is directly substituted as the numeric value "1.98," the result is exactly 10,610 people, matching the report's figure.
This suggests the example may involve a percentage-and-decimal confusion (1.98% vs. 1.98), or that "weighting" is expressed here in a non-standard way (for example, treating a percentage number directly as a coefficient).
Under either explanation, the discrepancy exposes a key ambiguity in how the formula is communicated: whether the impact-attribution ratio uses a decimal or a percentage number must be strictly defined. Otherwise, investors computing it independently can easily obtain results differing by a factor of 100, undermining the metric's reproducibility.
From a conceptual standpoint, the formula essentially allocates a company's total impact according to an "economic ownership proportion," i.e.:
\[
\text{investor's attributable impact} = \text{company total impact} \times \frac{\text{market value of investor's indirect holding via fund}}{\text{company total market capitalization}}
\]
Here, "market value of indirect holding" = investment amount × fund portfolio weight. Therefore, the formula itself follows the logic of bearing impact in proportion to capital contributed, but it also implies an important premise: company impact is a "stock attribute" rather than an "incremental attribute." The report also acknowledges that increasing investment does not expand the positive impact a company has already achieved in that year — which suggests that investors should not interpret the Impact Indicator as environmental or social productivity per unit of capital, but rather as a yardstick for capital-allocation entitlement.
The report uses a five-year time window rather than the current fiscal year to determine SDG contributions. This is a forward-looking mapping. For example, a company that has not yet delivered an environmentally beneficial product at scale, but whose pipeline clearly points toward a particular SDG, may still be counted as contributing within the next five years. This helps avoid myopic statistics, but it also introduces judgment risk — a company's strategy may shift within five years, distorting the mapping.
The report emphasizes that SDG mapping is conducted independently of company reporting, which weakens the greenwashing risk of corporate self-labeling. However, independent judgment depends on the subjective knowledge of the analysis team, and consistency must be ensured through internal audit or third-party verification. The phrase "Independent verification" at the end of the document implies that external assurance has been introduced, but the scope and criteria are not specified; this may be supplemented in later sections.
The Impact Data system presented in this section achieves a fine balance between "conservative" and "usable." It sacrifices some completeness of corporate data in exchange for aggregate figures that are less contestable. At the same time, three issues are worth tracking:
These methodological details determine the credibility of the Impact Report and are also the key points that the later sections, "Business Practices Reporting" and "Capital Chain," must address.
Deliberately separating the `Impact Report` (positive contributions of products and services) from `Positive Conversations` (corporate behavior and ESG practices) is a far-sighted "intellectual hygiene" measure. It avoids conflating "selling good products" with "being a good company," which is more honest than many peer funds claiming to be "holistic."
But this separation also creates new governance blind spots:
The report claims that an independent third party annually performs `Limited Assurance` over "selected information" in accordance with `ISAE (UK) 3000`. This sounds prudent, but its limitations need to be understood precisely.
| Dimension | Limited Assurance (limited assurance) | Reasonable Assurance (reasonable assurance) |
|---|---|---|
| Conclusion type | Negative-form assurance: no material misstatements identified | Positive-form confirmation: information is fairly stated in all material respects |
| Evidence gathering | Inquiry, analytical procedures, limited testing | Detailed testing, internal-control evaluation, substantive procedures |
| Probability of detecting misstatements | Lower | Higher |
| Report purpose | Internal governance, initial trust | Audit reports, regulatory disclosure |
At least two points in the text warrant caution:
It is also worth noting that the report uses emotive language such as "we hope that this methodology provides clients... comfort." In financial disclosure, "hope" is a weak word; a professional report should use "we believe," "we are confident," or simply state facts. This suggests that the preparers themselves are not entirely confident in the method's robustness.
The section on `The Capital Chain` tries to explain capital flows in plain-language terms, but it oversimplifies the actual structure of public markets and even contains two factual inaccuracies.
First inaccuracy: IPO capital attribution.
The text says, "If the company has its initial public offering, we buy shares directly from the company." In reality, the vast majority of IPOs are "combined offerings": they include both newly issued shares from the company (primary shares) and existing shares sold by founding shareholders/PE investors (secondary shares). Only the former directly provides capital to the company; the latter is a payment to selling shareholders. The wording creates the impression that all IPO proceeds flow onto the company's balance sheet.
Second inaccuracy: secondary-market purchases can still be described as "investing in the company."
When public-market investors buy existing shares of a listed company, the funds do not flow to the company. The report actually acknowledges this, yet still depicts the investment as a form of "support." The real support lies in:
These behaviors do have practical value, but the report does not mention one key tool: exit. According to Hirschman's Exit–Voice–Loyalty theory, when shareholders are dissatisfied with management, exit (selling shares) is a strong signal, sometimes more effective than voice. The `Positive Change` portfolio explicitly commits to a 5–10 year holding period, effectively restricting the exit option in advance.
This creates a risk: excessive loyalty may breed management inertia. If corporate behavior deteriorates, would the fund continue to hold in the name of "long-term support"? The report mentions no exception clauses or rapid-exit mechanisms. This narrative closure deserves client vigilance.
The CATL section details the methodology for estimating avoided emissions, but its core conclusion — more than 26.7 Mt CO₂e avoided in 2025 — rests on multiple highly optimistic assumptions. This can be unpacked layer by layer.
| Step | Assumption | Optimistic Direction | Potential Impact |
|---|---|---|---|
| EV batteries | Uses IEA's EV LCA calculator, compared with ICE | Uses regional average vehicles instead of actual CATL customer models | Overestimation or underestimation, direction uncertain |
| EV batteries | CATL's 39.2% market share directly mapped to the "share of enabled vehicles" | Ignores battery-capacity differences; different vehicles have different battery sizes; capacity share would be more accurate | Possibly overestimated |
| BESS | Battery charging comes entirely from solar power that would otherwise have been curtailed | In practice, much BESS charging is arbitrage or frequency-regulation charging, not all from curtailed power | Severely overestimated |
| BESS | FTM baseline is coal power in China and OCGT elsewhere | Ignores grid-decarbonization trends; by 2025, China's coal-power share had declined | Overestimated |
| BESS lifecycle | 90% efficiency, 80% DOD, 7,000 cycles (approx. 18 years) | Engineering ideal values; actual capacity degradation is non-linear | Overestimates later-stage output |
Particularly notable is the BESS charging assumption. The report states that it "assumed that the batteries are charged on solar power which would otherwise have been curtailed." This means that, as a counterfactual, without CATL's batteries those solar power units would have been "wasted" — so battery discharge displaces coal power or natural-gas peaking power. But working through the arithmetic:
Another data gap: CATL reports 121 GWh of BESS sales, but the full-year total of 26.7 Mt CO₂e in reduced emissions does not provide a breakdown between BESS and EV batteries. The text only mentions that EV batteries avoided 5.3 Mt CO₂e, implying that BESS contributed roughly 21.4 Mt CO₂e. Thus, although BESS sales volume may be lower than EV batteries (CATL's 2025 power-battery shipments far exceeded its energy-storage shipments), BESS contributed about 80% of the emission reductions. This in turn confirms that the "curtailment charging" assumption for BESS is the fulcrum of the calculated result.
The Coursera section exposes a common problem in social-impact measurement: substituting quantifiable inputs for unobservable outcomes. The core formula is:
> 97.5 million learner benefits = 107.2 million completed assessments × 91% self-reported benefit rate
This calculation has three serious flaws:
Flaw 1: Treating "completed assessments" as "learner count."
The text explicitly says that "each completed assessment represented one learner engaging meaningfully with course content." A learner could easily submit multiple assessments in the same course (e.g., mock exams, retakes) or complete multiple assessments across courses. At the end of 2025, registered learners were 197 million, but only 107.2 million assessments were completed — indicating that a large number of registrants did not complete assessments. Using an "activity volume" figure to stand in for "learner count" is inflated on both the numerator and denominator.
Flaw 2: The 91% self-reported benefit comes from a sample survey.
The `Learner Outcomes Report` typically covers only users who completed courses or took part in feedback surveys. Survey response rates on online education platforms are usually between 10% and 30%, and "getting a promotion/raise" is an indicator with high social desirability bias. There is no control group, and other factors such as economic cycles or job changes cannot be ruled out. Statistically, this resembles a "user satisfaction survey" more than a "causal impact evaluation."
Flaw 3: The multiplier only amplifies; it does not correct.
Even if the 91% estimate were accurate, multiplying it directly by completed assessments is equivalent to treating each assessment as an independent, equally probable event. The multiplier ignores the fact that the same person can benefit multiple times. For example, a learner who completes 10 assessments may experience only one promotion, but the formula counts 10 × 91% = 9.1 "possible beneficiaries."
The methodological nature of CATL and Coursera can be compared as follows:
| Dimension | CATL avoided-emissions estimate | Coursera learner-benefit estimate |
|---|---|---|
| Data type | Physical/engineering data + industry statistics | User self-reported surveys + platform behavioral data |
| Key assumptions | Curtailment charging, ideal cycle life | Number of assessments = number of learners |
| Causal-chain length | Short: battery → EV/storage → displaces fossil-fuel electricity | Long: course → skill improvement → promotion/salary increase |
| Error direction | Systematic overestimation | Systematic overestimation |
| Verifiability | Approximately verifiable via battery charge/discharge records and grid data | Almost unverifiable; no control experiment |
The "Introduction to Methodology" section previewed estimates for five companies — CATL, Coursera, Deere, Duolingo, and Rivian — but only CATL and Coursera were actually elaborated. As an introduction, this asymmetry hints at differences in measurement maturity across industries and types of impact.
More importantly, these estimation methods are mostly the product of third-party experts commissioned on a "best effort" basis. The original text uses phrases such as "commissioned an expert researcher" and "best effort estimate." This means:
In the impact-investing industry, such "customized estimates" are not uncommon, but placing them in the same report as the `ISAE (UK) 3000` assurance statement creates the illusion of being "audited." What is actually assured is only "Selected Information," and the biggest assumptions in the methodology — such as "curtailment charging" and "assessment = learner" — are precisely the ones most in need of external challenge.
These observations are not meant to deny the genuine efforts behind this project, but rather to remind readers: the precision of impact measurement can never be compared with that of financial measurement. In the next installment (Part 4/4), the broader concept of "positive chains" and the metaphor of "value chain transmission" are expected to be discussed — that will be the key to testing whether the framework has conceptual coherence, or is merely a collection of "impact stories."
Reading Deere, Duolingo, and Rivian side by side, the most valuable observation is not their respective emission-reduction figures, but rather that the verifiability of the three data foundations forms an evident descending/transitional spectrum:
| Case | Core Data Sources | Degree of Regulatory Auditing | Key Risks |
|---|---|---|---|
| Rivian | 10-K, shareholder letters, independent LCA literature | SEC-regulated; misstatements carry legal risk | Deviation of actual vehicle usage intensity from assumptions |
| Duolingo | 10-K (revenue) + public list price | SEC regulates revenue data; unit price is a public quote | Actual paid price ≠ list price; revenue scope definition |
| Deere | Company press releases + USDA survey | Self-published by the company; no third-party verification | Equivalence assumption between deployed acreage and treated acreage |
Rivian's 10-K is subject to U.S. securities laws, so its disclosed delivery volumes and financial data carry the highest credibility; Duolingo's revenue likewise comes from the 10-K, but the unit price per test is taken from the official website, and the quotient of the two is highly sensitive to the "actual average selling price"; Deere's 5 million acres come entirely from company press releases, with no record of independent verification. If research institutions disseminate these figures externally, they should conduct a clear sensitivity analysis on the credibility weighting of the data sources, rather than presenting the three side by side as equally reliable.
The "absolute figures" of the three cases differ dramatically:
| Case | 2025 Avoided Amount | Unit | Corresponding Physical Magnitude |
|---|---|---|---|
| CATL | 21,443,040 | tCO₂e | Roughly equivalent to one month of emissions from 46 million ICE passenger cars |
| Rivian | 417,121 | tCO₂e | Roughly one year of emissions from 90,000 ICE pickup trucks |
| Deere | 3,040 | metric tons of herbicide | Approximately 6.7M pounds of active ingredient |
This difference does not mean that CATL's "environmental contribution" is 7,000 times that of Deere; rather, it reflects different physical leverage across the three business models: energy storage replaces energy infrastructure measured in "GWh"; electric vehicles replace modes of transportation measured in "millions of vehicles"; precision agriculture optimizes the intensity of chemical inputs "per acre." When these three figures are placed side by side, the dimension of "avoidance density" (per unit of revenue, per unit of product, per user) must be clarified; otherwise, audiences will be misled in their judgments of scale and value.
Duolingo is the only case with no direct link to greenhouse gas emission reductions — it does not reduce emissions, does not replace fossil fuels, and does not lower chemical use. If the positioning is to hold, its logic would have to be "improved English proficiency → greater educational/employment mobility → enhanced climate adaptation capacity," but the text quantifies only the number of tests, leaving none of the links in this transmission chain quantified. This suggests to readers: in ESG impact assessment, quantified indicators from the social dimension (number of tests) and quantified indicators from the environmental dimension (tCO₂e) cannot simply be added together or placed side by side. The framework should clearly distinguish "climate-mitigation-type impacts" from "social-welfare-type impacts"; otherwise, the overall rigor of the methodology will be compromised.
Taken together, these four cases provide a clear "ladder of impact quantification":
| Tier | Characteristics | Applicable Case |
|---|---|---|
| Direct measurement | Direct metering at the equipment level; no scenario assumptions required | Rivian (LCA difference + measured mileage) |
| Indirect calculation | Based on company-disclosed data, converted with third-party parameters | Deere (press-release acreage + USDA application data) |
| Revenue back-calculation | Physical volume derived from financial data; affected by price assumptions | Duolingo |
| System modeling | Relies on complex assumptions about grid dispatch and curtailment scenarios | CATL |
Within these four tiers, the further down the ladder, the weaker the "auditability" of the results — but the guiding significance for policy and investment decisions is not necessarily weaker. What truly matters is not the precise number, but the "uncertainty bounds" and "directional bias notes" attached to each estimate — and this component is lacking in all four cases. If future revised versions could annotate sensitivity ranges in parentheses after each figure (for example, "-20% / +35%"), their methodological rigor would be markedly enhanced.