Mathematical definition
In exact terms, impact cents per dollar $$S\_{r,c,i}$$ of company $$c$$ and impact $$i$$ are defined as $$S\_{r,c,i}=\lambda\_i\frac{q\_{c,i}}{r\_c / R},$$ where * $$\lambda\_{i}$$ is the economic cost of impact $$i$$: this is used to make impact categories comparable between each other. Economic costs are explained in detail in [Monetization of impact](broken://pages/bYKTp8JQ4oQDUgZ26cQS). * $$q\_{c,i}$$ is the share of impact $$i$$ that company $$c$$ creates, relative to the total impact created by all companies. * $$r\_c$$ is the annual revenue that company $$c$$ generates. * $$R$$ is the total annual revenue generated by the private sector. It's worth noting that the ratio $$\frac{q\_{c,i}}{r\_c / R}$$ corresponds to the relative intensity at which company $$c$$ creates impact $$i$$. An impact intensity of 1.0 corresponds to the average intensity of all companies in the global economy, where anything below 1.0 is below this global average, and correspondingly values above 1.0 are above it. Ecnomic costs $$\lambda\_i$$ can be understood as the weights assigned to impact intensities of each impact category $$i$$.The UN Sustainable Development Goals (2015)
Geographical distribution of Upright's company coverage
| Fund type | Typical target |
|---|---|
| SFDR Article 9 funds | 100% |
| Equity funds that integrate impact/ESG considerations | 95% |
| Fixed income funds that integrate impact/ESG considerations | 85% |
The principal components of the net impact model, including their respective inputs and outputs
Products of Siemens
Industrial automation control systems, Automation engineering services, Systems engineering services, Linear motors, Industrial energy management systems, Enterprise systems integration consulting, Voltage converters, Air quality monitoring equipment, Ultrasound machines, Surgical x-ray machines, MRI machines, Medical laboratory automation systems, Medical imaging software, Medical image analysis software, Mammography machines, Management consulting for medical care, Hematology analyzers, Diagnostics software, CT scanners, Private clinical drug testing, Chest x-ray machines, Cardiac resynchronization therapy (CRT) devices, Blood gas monitors, Biochemical assay kits, Electric trams, Metro trains, Electric locomotive engines, Electric trains, Traffic management software, Train HVAC systems, Train bogies, Railway vehicle engineering services for electric railway vehicles, Railroad traction power supplies, Railway vehicle repair and maintenance, Railroad cars, Railroad car parts, Management consulting for the transportation industry, Hybrid locomotive engines, Smart electricity grid software, Process analytics software, Product lifecycle managementProducts for GE
Corporate credit granting, Gas turbines, Wind turbines, Wind turbine generators, Wind turbine gear boxes, Wind turbine maintenance, Maintenance engineering services for wind turbines, Real time information software, Repair and maintenance services of gas turbines, Management consulting for the energy industry, High-voltage capacitors, Energy efficiency consulting, Digital design consulting, Data science consulting, Smart electricity grid software, Distributed energy resources management software, Asset performance management software, Medical x-ray machines, X-ray imaging software, Ultrasound machines, Resuscitators, Nuclear medicine imaging scanners, MRI imaging software, MRI machines, Medical laboratory management software, Medical image enhancement software, Medical image analysis software, Medical ventilators, Medical alarm services, Health care management software, EEG monitors, ECG monitors, Computerized clinical decision support software, Contrast agents (V08), Repair, maintenance and installation of medical supplies and appliances, Anaesthetic machines, Military power supply systems, Military aircraft repair, Military aircraft engines, Aircraft jet engines, Aviation fleet management software, Aircraft wiring systems, Aircraft turboprop engines, Aircraft servicing, Air navigation software, Airplane inspection and maintenance, Aircraft control software, Air force command and control systems, Cloud infrastructure services, Electric motors, Cybersecurity management systems, Cybersecurity services, Cybersecurity assessment software, Selective laser melting machines, Process outsourcing services, Renovation of nuclear power plants, Thermal power turbines, Thermal power boilers, Steam generators, Power transformers, Process controllers, Nuclear waste disposal engineering, Nuclear fuel cycle engineering, Nuclear fuel handling services, Nuclear power engineering services, Nuclear decommissioning engineering, Nuclear power plant maintenance, Nuclear fuels, Natural gas generators, Modernisation of nuclear power plants, Modernisation of fossil fuel power plants, Diesel generators, Seafloor mapping services, Drilling rig auditing services, Plug and abandon engineering, Oil exploration services, Petroleum reservoir engineering, Oil refining equipment, Oil wellheads, Oil terminals, Oil production packers, Oil drilling risers, Oil pipes, Oil and gas industry chemicals, Offshore surveying services, Offshore oil rig engineering services, Oil drilling engineering services, Natural gas transfer pumps, LNG terminals, LNG pipes, Deepwater oil drilling machinery, Centrifugal compressors, Pelton turbines, Modernisation of hydropower plants, Kaplan turbines, Hydropower plant manifold systems, Hydropower plant penstocks, Francis turbines, Bulb turbinesProducts for Pfizer
Covid-19 vaccines, Apixaban, Palbociclib, Pneumococcal vaccines, Preclinical research services for physical health, Clinical research services for cancer, Clinical research services for genetic diseases, Clinical research services for deficiency diseases, Clinical research services for infectious diseases, Glucocorticoids, Cephalosporins, Tofacitinib, Enzalutamide, VEGFR tyrosine kinase inhibitors, Tetracyclines, Linezolid, Azithromycins, Clinical research services for vascular diseases, Clinical research services for the autoimmune system, Clinical research services for diabetes mellitus, Clinical research services for diseases of liver, Sunitinib, Sulfonamides and trimethoprim, Streptogramins, Quinolones, Polymyxins, Penicillins, Metronidazole, Macrolides, Lincosamides, Ertapenem, Etanercept, Daptomycin, Aminoglycosides, Varenicline, Rituximab, Crizotinib, Bosutinib, Tafamidis, Subcutaneous human immunoglobulin, Epoetin alfa, Blood coagulation factors, Bevacizumab, Meningococcal vaccines, Lorlatinib, Heparin, Estrone, Estradiol medication, Epinephrine, Dermatological corticosteroids (D07), Inotuzumab ozogamicin, Fesoterodine, Exemestane, Encorafenib, Dexmedetomidine, Desvenlafaxine, Avelumab, Mitogen-activated protein kinase (MEK) inhibitors, Infliximab, Zoledronic acid, Zaleplon, Yeast infection medicine, Treatment of wounds and ulcers (D03), Vitamin supplements, Vincristine, Venlafaxine, Vecuronium, Uterotonics, Ulcerative colitis medicine, Tuberculosis medication, Trimethobenzamide, Trifluridine, Triazolam, Trastuzumab, Topotecan, Tolterodine, Tick-borne encephalitis vaccines, Throat medication (R02), Thiazide diuretics, Theophylline, Testosterone medication, Temsirolimus, Tazobactam, Tavaborole, Talazoparib, Tacrolimus, Suxamethonium, Surgical sealant film, Sufentanil, Sterile water, Somatropin medication, Silver sulfadiazine, Sildenafil, SGLT2 inhibitors, Sertraline, Ropivacaine, Rocuronium bromide, Ramipril, Quinapril, Propofol, Promethazine, Prostaglandin E1, Procyclidine, Procainamide, Pregabalin, Potassium supplements, Potassium-sparing diuretics, Piroxicam, Phenytoin, Phenelzine, Pentostatin, Pentazocine, Pegvisomant, Pegfilgrastim, Paromomycin, Paricalcitol, Pancuronium, Pamidronate , Paclitaxel, Oxaprozin, Oxaliplatin, Oral contraceptives, Anti-obesity medication (A08), Norethisterone, Norepinephrine, Nitroglycerin, Naloxone, Nalbuphine, Nafarelin, Mucoactive agents, Morphine, Mitoxantrone, Metoclopramide, Methotrexate, Methimazole, Metaxalone, Mesuximide, Meropenem, Mepivacaine, Meperidine, Medroxyprogesterone acetate, Magnesium salts, Lorazepam, Low-ceiling diuretics, Liothyronine, Lidocaine, Levothyroxine, Levetiracetam, Laxatives, Latanoprost, Ketorolac, Ketamine, Irinotecan, Iron chelating agents (V03AC), Indometacin, Idarubicin, Ibutilide, Hydroxyzine, Hydromorphone, HIV medication, Histone deacetylase (HDAC) inhibitors, High-ceiling diuretics, Herpes medication, Hedgehog pathway inhibitors, Heartburn medication, Gonadotropin medication, Glipizide, Glibenclamide, Gemtuzumab ozogamicin, Gemfibrozil, Gemcitabine, Gaucher's disease medication, Gabapentin, Fosphenytoin, Fludarabine, Filgrastim, Fentanyl, Fenoldopam, Etomidate, Ethosuximide, Estramustine phosphate, Epirubicin, Enalapril, Electrolyte solutions for blood, Eletriptan, Echothiophate, Doxorubicin, Doxercalciferol, Doxepin, Dopamine, Dopamine agonists, Dofetilide, Docetaxel, Dobutamine, Disopyramide, Diphenhydramine, Diclofenac, Diazepam, Desmopressin, Dermatitis medication, Detoxifying agents for antineoplastic treatment (V03AF), Dacomitinib, Dacarbazine, Cytarabine, Cromoglicic acid, Cough suppressants, Copper supplements, Colestipol, Cisatracurium besilate, Chromium chloride, Chloroprocaine, Celecoxib, Ceftriaxone, Ceftazidime, Cefpodoxime, Cefoxitin, Cefotaxime, Cefoperazone, Cefepime, Cefazolin, Cardiac glycosides, Carboplatin, Calcium supplements, Calcium channel blockers (C08), Buprenorphine, Bupivacaine, Blood plasma proteins, Blood platelets, Blood plasma, Bleomycin, Bivalirudin, Beta blockers (C07), Atropine, Atracurium besilate, Atorvastatin, Argatroban, Antipsychotics, Antipropulsives, Antihypertensives (C02), Antifibrinolytics, Anti-thymocyte globulin, Antiemetics and antinauseants (A04), Amiodarone, Aminophylline, Alprazolam, Alpha-glucosidase inhibitors, Alfentanil, Afatinib, Adenosine, Absorbable gelatin compressed spongesCausality classification of articles given a product phrase and impact phrase (classes d=decreases, i=increases, n=no causality, o=other are simplified for clarity)
{% endhint %} # Generalization of scientific knowledge This page introduces how scientific knowledge is generalized in the Upright net impact model. Scientific and other relevant knowledge about products and their impacts may pertain to specific or generic product and service categories. Therefore products in the Upright net impact model are modelled as a hierarchy (i.e., parent-child) relationships. These relationships are used to understand what research is relevant to which specific products.
Simplified illustration of parent-child relations in the Upright product graph
Figure 1: Simplified illustration of value chain relations in the Upright product graph
| Negative valence | Impact category | Positive valence |
|---|---|---|
| N/A | Jobs | 10.01 |
| N/A | Taxes | 13.40 |
| N/A | Societal infrastructure | 14.26 |
| 1.75 | Societal stability | 0.67 |
| 0.39 | Equality & human rights | 0.23 |
| N/A | Knowledge infrastructure | 1.01 |
| N/A | Creating knowledge | 1.82 |
| 0.02 | Distributing knowledge | 2.43 |
| 8.32 | Scarce human capital | N/A |
| 7.35 | Physical diseases | 2.97 |
| 1.51 | Mental diseases | 0.38 |
| N/A | Nutrition | 3.64 |
| 0.34 | Relationships | 1.73 |
| 0.90 | Meaning & joy | 3.21 |
| 19.22 | GHG | 1.53 |
| 5.98 | Non-GHG | 0.69 |
| 1.84 | Scarce natural resources | 0.03 |
| 6.73 | Biodiversity | 0.06 |
| 4.20 | Waste | 0.49 |
Figure 1: Summary of the IOOI analysis conducted for costs related to GHG emissions
References
* *IPCC Sixth Assessment Report* * *OECD: Effective Carbon Rates 2021* * *OECD: Cost-Benefit Analysis and the Environment: Further Developments and Policy Use 2018* * *Ricke, K., Drouet, L., Caldeira, K. et al. Country-level social cost of carbon. Nature Clim Change 8, 895–900 (2018)* * *Carleton, Tamma and Greenstone, Michael, Updating the United States Government's Social Cost of Carbon (November 12, 2021). University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2021-04* * *Robert S. Pindyck, The social cost of carbon revisited, Journal of Environmental Economics and Management, Volume 94, 2019, Pages 140-160, ISSN 0095-0696* * *Richard S.J. Tol, A social cost of carbon for (almost) every country, Energy Economics, Volume 83, 2019, Pages 555-566, ISSN 0140-9883* * *Smith, S. and N. Braathen (2015), "Monetary Carbon Values in Policy Appraisal: An Overview of Current Practice and Key Issues", OECD Environment Working Papers, No. 92, OECD Publishing, Paris.* * *Jarmo S Kikstra et al 2021 Environ. Res. Lett. 16 094037: The social cost of carbon dioxide under climate-economy feedbacks and temperature variability* * *US: interagency working group (IWG) / Technical Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide* * *Brian C. Prest, Kevin Rennert, Richard G. Newell, and Jordan Wingenroth 2022: Social cost of carbon explorer* * *Rennert, K., Errickson, F., Prest, B.C. et al. Comprehensive evidence implies a higher social cost of CO2. Nature 610, 687–692 (2022)*| Category | Examples | Validity | Explanation |
|---|---|---|---|
| Broadly consumed goods | Literary fiction, music | ✅ Yes | The price reflects the observed preference of a large, representative sample of the general public. |
| Luxury goods | Gold jewelry | ❌ No | The price reflects the observed preference of a small minority. It is not representative of the general public. |
| Addictive substances | Alcoholic beverages | ❌ No | Physical addiction is expected to increase the demand for the product. |
The principal components of the net impact model, including their respective inputs and outputs
Code Snippet
```python products = { "TOOLS", "FERTI", "FRUIT", "APPLE", "PEARA", "APLJU" } parentage = { "APPLE": "FRUIT", "PEARA": "FRUIT" } leaves = [p for p in products if p not in parentage.values()] ```Code Snippet
```python impacts = { ("JOBS", "P"), ("SHC", "N"), ("HEALTH", "P"), ("ENVIRONMENT", "N") } ```Code Snippet
```python market_sizes = { "TOOLS": 23, "FERTI": 30, "FRUIT": 100, # sum of APPLE and PEARA "APPLE": 60, "PEARA": 40, "APLJU": 20 } total_global_market = sum(market_sizes[leaf] for leaf in leaves) market_shares = { product: market_size / total_global_market for product, market_size in market_sizes.items() } ```Code Snippet
```python value_adds = { "TOOLS": 5, "FERTI": 10, "FRUIT": 45, # sum of APPLE and PEARA "APPLE": 25, "PEARA": 20, "APLJU": 5 } flows = { ("TOOLS", "APPLE"): 15, ("TOOLS", "PEARA"): 8, ("FERTI", "APPLE"): 20, ("FERTI", "PEARA"): 12, ("APPLE", "APLJU"): 15, } ```Code Snippet
```python unnormalized_scores = {} ```Code Snippet
```python total_article_counts = { # N_p in the above "TOOLS": 250, "FERTI": 100, "FRUIT": 200, "APPLE": 100, "PEARA": 10, "APLJU": 50, } relevant_article_counts = { # R_{p,i} in the above ("FERTI", "JOBS", "P"): 1, ("FERTI", "ENVIRONMENT", "N"): 15, ("FRUIT", "HEALTH", "P"): 12, ("FRUIT", "ENVIRONMENT", "N"): 5, ("APPLE", "HEALTH", "P"): 4, ("APPLE", "ENVIRONMENT", "N"): 4, ("PEARA", "HEALTH", "P"): 5, ("PEARA", "ENVIRONMENT", "N"): 1, ("APLJU", "HEALTH", "P"): 1, } ```Code Snippet
```python def get_flattened_bibliometric_score(leaf, impact, valence): my_total_article_count = total_article_counts.get(leaf, 0) my_relevant_article_count = relevant_article_counts.get( (leaf, impact, valence), 0 ) parent = parentage.get(leaf, None) parent_total_article_count = total_article_counts.get(parent, 0) parent_relevant_article_count = relevant_article_counts.get( (parent, impact, valence), 0 ) if parent: siblings = [s for s in leaves if parentage.get(s, None) == parent] sibling_size = sum(market_sizes.get(s, 0) for s in siblings) my_relative_size = market_sizes.get(leaf, 0) / sibling_size else: my_relative_size = 1 return ( (my_relevant_article_count + my_relative_size * parent_relevant_article_count) / (my_total_article_count + my_relative_size * parent_total_article_count) ) unnormalized_scores[("HEALTH", "P")] = { leaf: get_flattened_bibliometric_score(leaf, 'HEALTH', 'P') for leaf in leaves } unnormalized_scores[("ENVIRONMENT", "N")] = { leaf: get_flattened_bibliometric_score(leaf, 'ENVIRONMENT', 'N') for leaf in leaves } ```Code Snippet
```python def get_flattened_supplemental_score(leaf, intensities): if leaf in intensities: return intensities[leaf] parent = parentage[leaf] return intensities[parent] ```Code Snippet
```python tertiary_education_ratios = { "TOOLS": 0.7, "FERTI": 0.4, "FRUIT": 0.2, "APLJU": 0.3 } average_education_ratio = sum( value * market_shares[product] for product, value in tertiary_education_ratios.items() ) score_shc = { product: education_ratio / average_education_ratio for product, education_ratio in tertiary_education_ratios.items() } unnormalized_scores[("SHC", "N")] = { leaf: get_flattened_supplemental_score(leaf, score_shc) for leaf in leaves } ```Code Snippet
```python turnovers_by_fte = { "TOOLS": 50, "FERTI": 40, "FRUIT": 20, "APLJU": 30 } average_turnover_by_fte = sum( value * market_shares[product] for product, value in turnovers_by_fte.items() ) score_jobs = { product: average_turnover_by_fte / turnover_by_fte # Note inverse for product, turnover_by_fte in turnovers_by_fte.items() } unnormalized_scores[("JOBS", "P")] = { leaf: get_flattened_supplemental_score(leaf, score_jobs) for leaf in leaves } ```| Product | ENVIRONMENT, N | SHC, N | JOBS, P | HEALTH, P |
|---|---|---|---|---|
| PEARA | 0.74 | 0.64 | 1.26 | 2.41 |
| APPLE | 0.71 | 0.64 | 1.26 | 1.13 |
| FERTI | 3.35 | 1.28 | 0.63 | 0 |
| TOOLS | 0 | 2.24 | 0.5 | 0 |
| APLJU | 0 | 0.96 | 0.84 | 0.44 |
Code Snippet
```python def normalize_scores(scores, market_shares): average = sum( scores.get(leaf, 0) * market_shares.get(leaf, 0) for leaf in leaves ) return { leaf: scores.get(leaf, 0) / average for leaf in leaves } leaf_intensities = { impact: normalize_scores(unnormalized_scores[impact], market_shares) for impact in impacts } ```| Participations (from-to) | PEARA | APPLE | FERTI | TOOLS | APLJU |
|---|---|---|---|---|---|
| PEARA | 20 | 0 | 12 | 8 | 0 |
| APPLE | 0 | 25 | 14.29 | 10.71 | 6.25 |
| FERTI | 4 | 6.67 | 10 | 0 | 1.67 |
| TOOLS | 1.74 | 3.26 | 0 | 5 | 0.82 |
| APLJU | 0 | 5 | 2.86 | 2.14 | 5 |
Code Snippet
```python from collections import defaultdict def determine_participations(product): relative_participations = [ { "target": product, "relative_flow": 1 }, *determine_relative_participations(product, 'd'), *determine_relative_participations(product, 'u') ] value_add = value_adds[product] participations = defaultdict(int) for relative_participations in relative_participations: target = relative_participations["target"] relative_flow = relative_participations["relative_flow"] participations[target] += value_add * relative_flow return participations def determine_relative_participations(product, direction): relative_flows = get_relative_flows(product, direction) target_ix = 1 if direction == 'd' else 0 my_relative_participations = [ { "target": od[target_ix], "relative_flow": relative_flow } for od, relative_flow in relative_flows.items() ] nested_relative_participations = [ { "target": y["target"], "relative_flow": x["relative_flow"] * y["relative_flow"] } for x in my_relative_participations for y in determine_relative_participations(x["target"], direction) ] return [*my_relative_participations, *nested_relative_participations] def get_relative_flows(product, direction): flows = get_flows(product, direction) source_ix = 0 if direction == 'd' else 1 return { od: flow / get_comparable_flow(od[source_ix], direction) for od, flow in flows.items() } def get_flows(product, direction): ix = 0 if direction == 'd' else 1 return { od: flow for od, flow in flows.items() if od[ix] == product } def get_comparable_flow(product, direction): if direction == 'u': return market_sizes[product] - value_adds[product] return market_sizes[product] participations = { (leaf, target): participation for leaf in leaves for target, participation in determine_participations(leaf).items() } df_participations = ( pd.Series(participations) .unstack(1, fill_value=0) .loc[leaves, leaves] ) ```| Inheritance (from-to) | PEARA | APPLE | FERTI | TOOLS | APLJU |
|---|---|---|---|---|---|
| PEARA | 0.78 | 0 | 0.31 | 0.31 | 0 |
| APPLE | 0 | 0.63 | 0.36 | 0.41 | 0.46 |
| FERTI | 0.16 | 0.17 | 0.26 | 0 | 0.12 |
| TOOLS | 0.07 | 0.08 | 0 | 0.19 | 0.06 |
| APLJU | 0 | 0.13 | 0.07 | 0.08 | 0.36 |
Code Snippet
```python inheritance_factors = ( df_participations .div(df_participations.sum(axis=0), axis=1) .loc[leaves, leaves] ) ```| Product | ENVIRONMENT, N | SHC, N | JOBS, P | HEALTH, P |
|---|---|---|---|---|
| APPLE | 1.06 | 1.13 | 1.11 | 0.77 |
| APLJU | 0.63 | 0.94 | 0.89 | 0.58 |
| TOOLS | 0.24 | 0.69 | 0.56 | 0.55 |
| FERTI | 1.25 | 0.75 | 0.91 | 0.91 |
| PEARA | 1.35 | 1.19 | 1.21 | 1.87 |
Code Snippet
```python market_sizes_v = pd.Series(market_sizes) def compute_full_intensities(organic_intensities): intensities_v = pd.Series(organic_intensities).reindex(leaves, fill_value=0) return ( inheritance_factors.T .dot(intensities_v * market_sizes_v[leaves]) .div(market_sizes_v[leaves]) .loc[leaves] ) df_leaf_intensities = pd.DataFrame(leaf_intensities) full_intensities = pd.DataFrame({ impact: compute_full_intensities(leaf_intensities[impact]) for impact in impacts }) ```| Product | ENVIRONMENT, N | SHC, N | JOBS, P | HEALTH, P |
|---|---|---|---|---|
| APPLE | 1.06 | 1.13 | 1.11 | 0.77 |
| APLJU | 0.63 | 0.94 | 0.89 | 0.58 |
| TOOLS | 0.24 | 0.69 | 0.56 | 0.55 |
| FERTI | 1.25 | 0.75 | 0.91 | 0.91 |
| PEARA | 1.35 | 1.19 | 1.21 | 1.87 |
| FRUIT | 1.17 | 1.16 | 1.15 | 1.21 |
Code Snippet
```python leaves_of_fruit = ['APPLE', 'PEARA'] weight = pd.Series([market_sizes[n] for n in leaves_of_fruit], index=leaves_of_fruit) fruit_impact_intensity = ( weight .dot( full_intensities.loc[leaves_of_fruit] ) .div(weight.sum()) .to_frame() .T .set_index(pd.Index(['FRUIT'])) ) full_intensities_all = pd.concat([full_intensities, fruit_impact_intensity]) ```| Product | ENVIRONMENT, N | HEALTH, P | JOBS, P | SHC, N |
|---|---|---|---|---|
| APPLE | 1.69 | 0.93 | 1.33 | 0.91 |
| APLJU | 1.01 | 0.7 | 1.07 | 0.75 |
| TOOLS | 0.38 | 0.66 | 0.67 | 0.56 |
| FERTI | 2 | 1.09 | 1.09 | 0.6 |
| PEARA | 2.16 | 2.25 | 1.45 | 0.95 |
| FRUIT | 1.88 | 1.46 | 1.38 | 0.93 |
Code Snippet
```python size_factors = pd.Series({ ("JOBS", "P"): 1.2, ("SHC", "N"): 0.8, ("HEALTH", "P"): 1.2, ("ENVIRONMENT", "N"): 1.6 }) product_rel_scores = full_intensities_all * size_factors ```| Product | NIR |
|---|---|
| APPLE | -0.15 |
| APLJU | 0 |
| TOOLS | 0.29 |
| FERTI | -0.19 |
| PEARA | 0.16 |
| FRUIT | 0.01 |
Code Snippet
```python positive = [(c, v) for c, v in impacts if v == 'P'] negative = [(c, v) for c, v in impacts if v == 'N'] def compute_nir(rel_scores): sum_p = rel_scores.loc[:, positive].sum(axis=1) sum_n = rel_scores.loc[:, negative].sum(axis=1) return ((sum_p - sum_n) / sum_p).rename('NIR') ```| Company | NIR |
|---|---|
| EQUIPMENT_CO | 0.04 |
| FRUIT_CO | 0.01 |
Code Snippet
```python companies = { "EQUIPMENT_CO": { "FERTI": 0.4, "TOOLS": 0.6 }, "FRUIT_CO": { "FRUIT": 0.8, "APLJU": 0.2 } } def compute_company_scores(product_set): ps_v = pd.Series(product_set) p_ix = ps_v.index return product_rel_scores.loc[p_ix, :].T.dot(ps_v) company_rel_scores = pd.DataFrame({ company: compute_company_scores(product_set) for company, product_set in companies.items() }).T company_nirs = compute_nir(company_rel_scores) ```Code Snippet
```python from scipy.stats import gamma alpha, beta = 8, 2 # scipy parametrizes the gamma with scale that is the inverse of beta lb, ub = gamma.cdf(x=2, a=alpha, scale=1/beta), gamma.cdf(x=6, a=alpha, scale=1/beta) ub - lb ```## GHG Emissions The GHG emission metrics measure the absolute and relative amount of carbon dioxide equivalents companies produce directly and indirectly. The metrics follow the definition the Greenhouse Gas Protocol, classifying companies' emissions into three scopes: * **Scope 1**: Direct emissions from owned and controlled resources. * **Scope 2**: Indirect emission from the purchased electricity, steam, heat and cooling. * **Scope 3**: All of the indirect emissions included in the value chain, covering both upstream and downstream emissions. Upright's estimates for all three scopes follow the same principle: companies' emissions are mainly driven by the economic activities that they conduct. Therefore, in Upright's estimates the detailed product and service mix of a company is used to explain and predict the resulting GHG emissions. In other words, companies with similar economic activities are expected to produce similar relative volumes of emissions. As with all of the Uprights metrics the disclosure data comes from publicly available data sources. For GHG emissions the values are fetched primarily from companies' sustainability reports. Upright's collected disclosure data and estimates mainly pertain to absolute Scope 1, 2 and 3 emissions. They are used to derive the following values: * **Carbon footprint (Scope 1 and 2)**: the sum of Scope 1 and Scope 2 emissions * **Carbon footprint (Scope 1, 2 and 3)**: the sum of Scope 1, 2 and 3 emissions * **GHG intensity (Scope 1 and 2)**: Carbon footprint (Scope 1 and 2) divided by the company revenue in €M * **GHG intensity (Scope 1, 2 and 3)**: Carbon footprint (Scope 1, 2 and 3) divided by the company revenue in €M {% hint style="info" %} **Accuracy of GHG estimates** The accuracy of Upright's models depend on the scope and the economic activities undertaken by the subject company. Scope 1 tends to be the most accurate estimate due to two reasons: * More companies report scope 1 emissions relative to scope 2 or comprehensive scope 3 emissions. * There exists little ambiguity in the computation logic that disclosing companies follow. For Scope 3 emissions this is the exact opposite: * Companies are less likely to disclose Scope 3 emissions. * Much of the calculation rules for Scope 3 emissions leave room for interpretation. Two very similar companies in terms of revenue mix may report very different Scope 3 emissions. {% endhint %} {% hint style="info" %} **PCAF Data Quality Score**\ Upright’s estimated GHG emissions data is classified as PCAF Data Quality Score 4. The Partnership for Carbon Accounting Financials (PCAF) provides a standard for measuring financed emissions, assigning a data quality score from 1 (verified reported data) to 5 (broad industry averages). Upright’s approach corresponds to **Option 3a** within the[ PCAF Global GHG Accounting and Reporting Standard](https://carbonaccountingfinancials.com/files/downloads/PCAF-Global-GHG-Standard.pdf). Upright achieves **Score 4** by utilizing product-level GHG estimation in combination with company revenue shares instead of relying on sector-level proxies. This approach ensures that where reported data is unavailable, the estimation reflects the specific economic activities of the company with a higher degree of fidelity than standard industry-average proxies. {% endhint %} {% hint style="info" %} **Minimum acceptable set of Scope 3 categories** Upright considers disclosures valid only if they include the major Scope 3 categories such as *“Use of sold products”* and *“Purchased goods and products”*. Vice versa disclosures do not need to include categories like *"Employee commuting"*, since they rarely amount to a material share of a comprehensive Scope 3 disclosure. {% endhint %} {% hint style="info" %} **Market-based and location-based Scope 2 emissions** Upright uses location-based Scope 2 emissions, as it is more practical to estimate. Market-based emissions allow for a “residual share” of location-based emissions when market-based emissions are not provided, leading to market-based disclosures being a mix of location-based emissions and very company specific market-based emissions. {% endhint %} {% hint style="info" %} **Geographical differences** The currently available volume of disclosure data does not allow for granularity on geographical differences. {% endhint %} ## Fossil fuel sector activity Activity in the fossil fuel sector indicates whether the company derives any revenue from * Exploration, mining, extraction, distribution or refining of hard coal and lignite * Exploration, extraction, distribution (including transportation, storage and trade) or refining of liquid fossil fuels * Exploring and extracting fossil gaseous fuels or from their dedicated distribution (including transportation, storage and trade) Upright's estimates are based on the detailed economic activity modelling of companies: as the regulation calls to identify whether any revenue has been derived from the above activities, it is important to inspect the full sector footprint of conglomerates and other companies operating in multiple industries. This revenue mix -based algorithm has been validated against publicly available exclusion lists and other listings of companies known to match the regulation's definition. The vast majority of discrepancies between the algorithm and the publicly available data correspond to multi-sector companies correctly captured by the algorithm while missed by the publicly available data. ## Non-renewable energy share Non-renewable energy share measures the share of non-renewable energy consumption of the total energy consumption of the company. {% hint style="info" %} Note that Upright's metric for non-renewable energy share deviates from the regulation's definition of "Share of non-renewable energy consumption and production" in the following manner: non-renewable energy share only measures the consumption mix of a company due to the scarcity of disclosed energy mix data for energy production. {% endhint %} Upright's estimates of non-renewable energy share are based on the detailed economic activity modeling of companies as well as their geographic footprint. Materially the energy mix of companies is expected to be similar between companies with similar economic activities. Upright's estimates weight the geographic average energy mix more for companies where limited disclosure data is available for similar companies. As with all of Upright's PAI metrics, the data comes from publicly available data sources. For energy mix data, this primarily includes direct company disclosures from sustainability reports, usually in the form of Global Reporting Initiative disclosure 302-1. ## Energy consumption intensity per high impact climate sector Energy consumption intensity measures the energy consumption relative to revenue separately for each so-called high impact climate sector. These sectors are defined as NACE Rev. 2 Sections A-H and L. The Upright platform reports on this metric in three separate values for each company: * **High impact climate sector**: the sector classification of the company, if it is any of the above-mentioned sectors. * **Total energy consumption**: absolute energy consumption of the company, reported in MWh. This metric has been made visible separately from the intensity value since most energy consumption disclosures are reported in absolute terms. * **Energy consumption intensity**: Total energy consumption converted to GWh and divided by the company revenue in € millions. Upright's estimates for energy consumption follow the same principle as for GHG emissions: companies' energy consumption is mainly driven by the economic activities that they conduct. Therefore, in Upright's estimates the detailed product and service mix of a company is used to explain and predict the resulting energy consumption. In other words, companies with similar economic activities are expected to consume similar volumes of energy. As with all of Upright's PAI metrics, the data comes from publicly available data sources. For energy consumption data, this primarily includes direct company disclosures from sustainability reports, usually in the form of Global Reporting Initiative disclosure 302-1. ## Activities negatively affecting biodiversity-sensitive areas Activity negatively affecting biodiversity-sensitive areas indicates whether all of the following criteria apply: * The company conducts activities in biodiversity-sensitive areas, as defined by the SFDR Regulatory Technical Standards. * These activities lead to the deterioration of natural habitats and the habitats of species and to disturbance of the species for which the protected area has been designated. * Conclusions or necessary mitigation measures identified in relevant assessments have not been implemented accordingly. Currently all positive indicator values for this indicator come from direct company disclosures, because outside-in estimation of the above company-specific criteria is not feasible. As with all of Upright's PAI metrics, the data comes from publicly available data sources. For biodiversity harms data, this primarily includes direct company disclosures from sustainability reports, usually in the form of Global Reporting Initiative disclosure 304. ## Emissions to water Emissions to water refer to direct emissions of: * Priority substances as defined in article 2(30) of directive 2000/60/EC. * Direct nitrates, phosphates, pesticides as defined in directives 2000/60/EC, 91/676/EEC, 91/271/EEC and 2010/75/EU. Upright's estimates of these water emissions are based on aggregate emission intensities of the above substances, grouped by economic activity. These emission intensities are applied to the modelled economic activities of each company to yield the estimated emission intensities and resulting absolute emission volumes. Direct company disclosure matching the above-mentioned definition is relatively rare as of early 2025. This leads to most values in the Upright platform relying on estimates. ## Hazardous waste Hazardous waste refers to hazardous waste as defined in article 3(2) of directive 2008/98/EC as well as radioactive waste. Upright's estimates of these hazardous waste emissions are based on aggregate waste intensities, grouped by economic activity and matching the above-mentioned definition. These waste intensities are applied to the modelled economic activities of each company to yield the estimated waste intensities and resulting absolute waste volumes. Direct company disclosure matching the above-mentioned definition is relatively rare as of early 2025. This leads to most values in the Upright platform relying on estimates. ## Production of chemicals Manufacture of chemicals as defined the regulation means activities that fall under the NACE Rev. 2 Class 20.2, "Manufacture of pesticides and other agrochemical products" Upright's estimates are based on the detailed economic activity modelling of companies: as the regulation calls to identify whether any revenue has been derived from the above activities, it is important to inspect the full sector footprint of conglomerates and other companies operating in multiple industries. Furthermore it is important to inspect agrochemical manufacturers closely to verify whether they only manufacture fertilisers or nitrogen compounds that fall outside the scope of Class 20.2. ## UNGC/OECD norm violations The norm violation metric indicates whether the company has been involved in violations of the UNGC principles or OECD Guidelines for Multinational Enterprises. As with all of Upright's PAI metrics, the data comes from publicly available data sources. For norm violations, this mainly consists of a broad scan of published norm violations of companies, including exclusion lists by 90+ financial institutions as collected by [Financial Exclusions Tracker](https://financialexclusionstracker.org/exclusion-list). Both explicit statements on UNGC/OECD norm violations (e.g., "Violation of international standards") and more specific activities that imply norm violations (e.g., "Involvement in anti-personnel mines") are considered. In general, a norm violation flag is given only if five or more investors have excluded the company. Currently no algorithmic estimation is conducted for norm violations because outside-in estimation of company-specific violations is not feasible. Companies with no violations detected in the comprehensive search are expected to not be in violation of the norms. ## UNGC/OECD compliance mechanisms The compliance mechanism metric indicates whether the company has published mechanisms to comply with UNGC principles or OECD Guidelines for Multinational Enterprises. It is worth noting that a company may by in compliance with these societal norms without having published formal compliance with the norms. For example, an independent hair saloon is unlikely to violate societal norms but likewise unlikely to publish compliance with the norms. As with all of Upright's PAI metrics, the data comes from publicly available data sources. For compliance mechanisms, this mainly consists of known lists of signatory companies. Currently no algorithmic estimation is conducted for compliance mechanisms because outside-in estimation of company-specific compliance mechanisms is not feasible. Companies with no compliance mechanisms detected in the comprehensive search are indicated as not having known compliance mechanisms. ## Unadjusted gender pay gap Unadjusted gender pay gap aims to measure possible gender discrimination in the workplace. It is measured by the difference between average gross hourly earnings of male paid employees and of female paid employees as a percentage of average gross hourly earnings of male paid employees. The factors that contribute to the broad definition of unadjusted gender pay gap have been studied by [Leythienne & Ronkowski, (2018)](https://op.europa.eu/en/publication-detail/-/publication/fb389f61-6f7c-11e8-9483-01aa75ed71a1/language-en) and [Blau & Kahn (2017)](https://www.aeaweb.org/articles?id=10.1257/jel.20160995) among others. These factors can be split into two categories: personal & job level -related factors like age, occupation and employment contract, and enterprise-related factors like company size and its principal economic activities. Upright's estimates are based on the enterprise level contributors. Using Upright's detailed economic activity modeling for companies enables a fluent representation of differences between companies. In addition, companies are grouped based on the employees count and revenue. The estimates are expected to outperform a naive economic sector and country average aggregates. As with all of the Upright's estimates the data comes from publicly available data sources. This includes direct company disclosures from sustainability reports and national databases. Currently the coverage of disclosure data is mainly driven by varying disclosure requirements and practices between countries: the United Kingdom makes pay gap data for large companies completely public while some countries outright ban disclose of pay-related data. ## Board gender diversity Board gender diversity measures the share of female members in the board of directors. Upright's estimates of board gender diversity are based on the economic activity modeling of companies as well as their geographic footprint. Materially the board composition of companies is expected to be similar between companies with similar economic activities and similar geographic location. As with all of Upright's PAI metrics, the data comes from publicly available data sources. For board gender diversity data, this primarily includes a comprehensive search of publicly available board member lists and their automatic classification into male and female members. ## Involvement in controversial weapons The compliance mechanism metric indicates whether the company is involved in the manufacturing of any of the following weapons: * anti-personnel mines * cluster munitions * chemical weapons * biochemical weapons It is worth noting that the list as defined by regulation excludes some weapons often understood as controversial, such as nuclear weapons. As with all of Upright's PAI metrics, the data comes from publicly available data sources. For the above-mentioned weapons, there are relatively few known manufacturers, and furthermore a large share of these manufacturers are governmental organizations or fully government owned. The remaining set of companies with any security issuance has been comprehensively screened via publicly available information, including exclusion lists by 90+ financial institutions as collected by [Financial Exclusions Tracker](https://financialexclusionstracker.org/exclusion-list). Nuclear weapons and other weapons not matching the regulation's definition have been excluded from consideration. Companies not found in these listings are expected to not be active in manufacture of these weapons.
## Current limitations * Upright continuously adds new disclosures from companies. In addition to providing access to more disclosures, Upright expects that upcoming additions of disclosed data will also improve the accuracy of the modelled estimates. * The data is still lacking some metadata, such as the details on norm violations. * Aggregates for funds and other portfolios are not available. Anyhow, API customers can do the aggregation based on company level data * Estimates of renewable energy production are not yet available for companies producing energy. The accuracy of the produced indicators is primarily limited by available information. Upright continuously seeks to improve the accuracy of its indicators by using the best available information and statistical methods for integrating information from different sources. # EU taxonomy This page provides information on how Upright produces data for metrics related to EU taxonomy of sustainable activities (EU regulation 2020/852) {% hint style="info" %} This page describes how Upright produces EU taxonomy metrics. For information on **what** EU taxonomy metrics Upright provides, visit [this page](/metrics/eu-taxonomy). {% endhint %} Upright seeks to produce all its impact data based on the best available information. Upright's EU taxonomy metrics are either: 1. Based directly on a company's reporting of taxonomy metrics or 2. Estimated by Upright based on other available information ## Used definitions The EU taxonomy metrics reflect the proportion of companies' turnover, CapEx or OpEx that relates to each sustainability objective defined by the EU. The EU has defined **6 environmental objectives**: 1. Climate change mitigation 2. Climate change adaptation 3. The sustainable use and protection of water and marine resources 4. The transition to a circular economy 5. Pollution prevention and control 6. Protection and restoration of biodiversity and ecosystems. Upright provides disclosed metrics for turnover-, CapEx- and OpEx-based figures when they are available. For turnover-based figures, Upright additionally provides estimated figures. Estimates are based on the following specifications: * **For the last four objectives:** Specification published in June 2023 (in this [Enviromental Delegated Act](https://finance.ec.europa.eu/system/files/2023-06/taxonomy-regulation-delegated-act-2022-environmental_en_0.pdf) from the European Commission) * **For the first two objectives:** Specification published on June 2021 (as part of the [Climate Delegated Act](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32021R2139)) ## Estimation of EU taxonomy metrics Data sources used for producing taxonomy metrics include companies' annual reports, companies' sustainability reports, companies' reporting to the U.S. Security and Exchange Commission (SEC), company websites, market statistics, market research, and environmental research. Additionally, some companies disclose supporting information directly to Upright. ### Estimation of taxonomy eligibility Production of estimated turnover-based EU taxonomy eligibility figures requires: 1. The classification of each company's activities in line with the economic activities covered by the EU taxonomy regulation 2. Determination of the revenue shares relating to each activity Most companies report their revenue only with a coarse subdivision (or no subdivision at all) that is insufficiently granular to allow for direct determination of revenue shares related to each EU taxonomy activity. Upright produces estimated revenue breakdowns by activity by using the revenue subdivisions reported by the company as a starting point and refining it based on market statistics, other market research, and quantitative metrics produced from company publications (websites, annual reports, regulatory filings) that correlate with the revenue shares being estimated. More information is available in [this article](/methodology/net-impact/overview-of-the-upright-net-impact-model/estimation-of-company-product-mixes). ### Estimation of taxonomy alignment In the general case, companies that don't report taxonomy alignment also do not publish sufficient information for a definite assessment of compliance with: * The substantial contribution criteria * The activity-specific DNSH criteria * The minimum safeguards Therefore, these criteria are considered in a probabilistic manner, assessing the likelihood at which each of a company's products linked to an EU taxonomy activity meets each criterion. In situations where specific information pertaining to the satisfaction of DNSH or minimum safeguards criteria is available (such as information on human rights violations), such information takes priority over the probabilistic assessment. The probabilities are calibrated to reflect *most likely* alignment (rather than, for example, minimum alignment). As required by the EU taxonomy regulation, minimum safeguards are considered on activity-level, rather than company-level. For this reason, a company with norm violations may still have taxonomy-aligned activities, as long as those norm violations are not related to the aligned activities. {% hint style="warning" %} **Limited accuracy** The accuracy of the taxonomy indicators is primarily limited by available information. Upright continuously seeks to improve the accuracy of its indicators by using the best available information and statistical methods for integrating information from different sources. {% endhint %} {% hint style="info" %} **Official role of estimated EU taxonomy figures** According to the European Securities and Market Authority (ESMA), the use of estimates for EU taxonomy figures is allowed when public disclosures are not available. The topic was discussed in the Q\&A published by ESMA in November 2022 as follows: *\[When] complete, reliable and timely information could not be obtained, financial market participants are allowed to make complementary assessments and estimates on the basis of information from other sources* Given this official position, estimated EU taxonomy figures will remain relevant to investors as long as a significant share of their investee companies do not disclose EU taxonomy figures. With the arrival of CSRD, the share of companies for which company-disclosed taxonomy figures are available is expected to grow in the coming years, which will somewhat reduce the necessity of using estimates. This will still leave many companies, such as those based outside of the EU and startups, out of scope. {% endhint %} # CSRD Double materiality {% hint style="info" %} Documentation on Upright's double materiality methodology is provided in **PDF format**. Get your copy [here](https://www.uprightproject.com/products/csrd-double-materiality-assessment/?lfid=documentation-download-csrd). {% endhint %} # Release cycle This page describes Upright's data release cycle. Impact measurement is a maturing field where **methodology is actively developed**. To offer the most up-to-date information to users, Upright regularly makes available new releases of Upright data. ## Model release process Three types of releases are available for logged-in users: 1. Latest releases (`latest`) 2. Current stable release (`stable`) 3. Previous stable releases The `latest` release may receive targeted updates and fixes to company product information, other company disclosures or the exact product information used to quantify the net impact of a company. After thorough testing and validation, `latest` is released as the current stable release. A new `latest` release is then created, reflecting the latest work in net impact quantification, regulatory methodology and other components of Upright’s results. The `latest` release is available to anonymous users and is selected by default for logged-in users. The current stable release is recommended for most use cases that require results remaining immutable. Previous stable releases remain available for logged-in users to allow for managed transitions. When a new latest release is released, material changes in results since the previous stable release are summarized in the Release notes of the release. By convention, stable releases follow a numbering scheme in the format `x.y.0`. {% hint style="info" %} **Using previous stable releases** Previous stable releases are useful for verifying how results looked back in time, for example when figures were last time reported. Users of Upright data may themselves determine when is the most appropriate time for them to migrate to new data releases. {% endhint %} ## Reasons for changes in results Upright results change for three underlying causes. 1. Changes to **input data** (e.g., integration of nutritional values or addition of new value chain links) 2. Changes in **definition and methodology** (e.g., delegated act to EU taxonomy that includes nuclear power and natural gas as potentially taxonomy aligned) 3. Changes to **companies themselves** (e.g., product revenue mix information) Release notes have more detailed information about result changes between two stable releases. {% hint style="info" %} **Restated annual time series data for companies** Upright is working to provide restated time series data for companies. Once this is available, each release will contain a *restated* annual time series for each company in which changes across time will reflect only changes to the companies themselves. Restatements can give answers to questions like "*how does the impact of a company develop during 2005-2022, based on the current best understanding of impact measurement*" For more information, contact your Customer Success Manager or Sales Representative. {% endhint %} # Release notes This section collects together release notes starting from release 0.4.0 ## Latest release Release notes for the latest release 1.12.0 are available [here](/releases/release-notes/1.12.0-04-2026#release-notes-04-2025). ## Current stable release Release notes for the current stable release 1.11.0 are available [here](/releases/release-notes/1.11.0-12-2025). ## Previous stable releases Release notes for previous stable releases starting from release 0.4.0 are available under this page # 1.12.0 (04 / 2026) Release notes for release 1.12.0. ## Release notes 04/2026 Release number: 1.12.0\ Release date: 27th April, 2026\ Notes updated: 27th April, 2026\ Current state: `Latest` The most significant improvements to the Upright model compared to the previous release 1.11.0 are listed below. `Latest` release 1.12.0 may receive targeted updates and fixes before it becomes `stable`, at the time a new latest release is published. These release notes will be updated to reflect any significant updates. More information on releases can be found on page [Release cycle](/releases/release-cycle). ### **Company coverage** * A set of companies has been updated based on reported 2025 revenue splits. More companies will be updated in latest 1.12.0 during May as new annual reports become available. ### Net impact * Update to the defence industry net impact figures * Defence industry product and company results have undergone significant updates, particularly within the Societal stability impact category. The updated methodology better reflects the potential societal benefits of military capabilities in defensive contexts, whereas previous assessments placed relatively stronger emphasis on offensive/attack use cases. The update is based on an expanded body of scientific literature, improving the robustness and balance of the underlying assessments * Following the update, there also is a clearer distinction between products primarily associated with offensive versus defensive use. Products with a predominantly defensive role are now assessed more positively in terms of Societal stability, while those associated with offensive use cases receive comparatively lower scores * Disclaimer: Upright does not take a political stance on defence. For example, in cases where weapons are not clearly designed solely for offensive or defensive use, Upright does not assess their real-world application context. Instead, Upright relies on scientific consensus regarding defence and weapons, while acknowledging that societal impact can vary significantly depending on the political context (e.g. attack vs. defence dynamics). * Updates to positive GHG emissions impacts, making the net impact results more positive for climate solutions * The modelling and economic cost calculation approaches for the GHG emissions impact category (positive valence) have been updated. An extensive revision was required due to the rapid recent growth of low-carbon solutions and new technologies on the market that contribute to reducing greenhouse gas emissions * On average, positive GHG emissions figures are now approximately 30–40% higher than in the previous model version. However, this reflects overall averages and does not apply uniformly across all products and services – increase/decrease may vary significantly depending on the specific solution * As part of the update, positive GHG emissions figures now also exhibit greater differentiation across products and services. This improved granularity makes it easier to identify solutions with particularly high carbon reduction potential * In addition to these targeted updates, Upright is working on a more profound Data Engine improvements, which will be released later this year ### CSRD double materiality assessment * Pro Tier has been published. The new tier allows: * Unlimited iterations of products and service lists. * Integrating firm-specific sustainability data points and geographic value chain details. * Adding, editing, or disabling impacts, risks, and opportunities (IROs) or adjusting materiality thresholds according to them. * Inviting colleagues to join the assessment and work together in real-time. * Upgrade with an embedded Stripe checkout by credit card. * Operational input data can be provided directly on the Upright platform for additional assessment breadth. * Assessment can be made more specific and granular by providing information about operational indicators, geographical footprint, and upstream industry exposure. * (Only available on paid DMA tiers) ### SFDR Principal Adverse Impacts (PAI) * PAI disclosures have been updated across metrics from the latest sustainability reports available. Upright’s disclosure collection focuses on the following, most widely reported metrics: * GHG Scope 1,2,3 emissions * Hazardous waste * Emissions to water * Total energy consumption * Non-renewable energy share * Anti-corruption / anti-bribery policies * Human rights policy * Due to corporate reporting cycles, some 2025 reports may not be available during the initial collection period. New data will be added to the database as companies release their official reports. * Increased anti-scraping measures on corporate websites have impacted automated report collection. These technical barriers may result in lower collection yields for newer years and reduced coverage for metrics relying on company reporting. ### EU taxonomy * EU-taxonomy disclosures have been extracted from latest sustainability reports available. * Due to corporate reporting cycles, some 2025 reports may not be available during the initial collection period. New data will be added to the database as companies release their official reports. * Increased anti-scraping measures on corporate websites have impacted automated report collection.These technical barriers may result in lower collection yields for newer years and reduced coverage for metrics relying on company reporting. * From stable release 1.11.0 onwards, Upright ceased collecting TCCT disclosures, but continues to provide TCCT estimates. Companies have ceased reporting disclosures for the objective Climate change mitigation or adaptation (TCCT), and are now reporting the union of all six objectives (TTOT). ### Real-time modeling of companies (add-on module) {% hint style="info" %} Available as an add-on module — not included in existing subscriptions. {% endhint %} * Instant company analysis for investment decisions: Add any company — including privately held firms — by entering basic details (name, website, revenue, employee count) and receive a full sustainability profile within minutes. * AI-powered product and service mapping: The tool automatically identifies a company's products and services using AI, building the foundation for a science-based impact and materiality assessment without manual data gathering. * Automated Sustainability Assessment: Each company added goes through an automatic Net Impact, DMA, SDG alignment, and other Upright assessments, giving investors a clear view of material sustainability risks and opportunities tied to the target company, and how a deal would affect fund sustainability metrics. * Private and secure: Custom companies created through the DD flow are private and not visible to other users, ensuring confidentiality during sensitive investment processes. # 1.11.0 (12 / 2025) Release notes for release 1.11.0. ## Release notes 12/2025 Release number: 1.11.0\ Release date: 3rd December, 2025\ Notes updated: 3rd December, 2025\ Current state: `Stable` The most significant improvements to the Upright model compared to the previous release 1.10.0 are listed below. `Latest` release 1.11.0 may receive targeted updates and fixes before it becomes `stable`, at the time a new latest release is published. These release notes will be updated to reflect any significant updates. More information on releases can be found on page [Release cycle](/releases/release-cycle). ### **Company coverage** * A set of companies has been updated based on reported 2024 revenue splits. More companies will be updated in latest 1.11.0 during December. ### CSRD double materiality assessment Upright has launched the market's first fully self-service double materiality assessment tool: * Self service double materiality assessment allows users to understand their evidence-based impact, risks, and opportunities (IROs) in minutes rather than months. * Not only a nice-looking matrix, but actionable insights down to individual products and their contribution to your company’s sustainability. * Users can get started with pre-populated products and services that drive identification of the IROs. These can be freely iterated by e.g. updating revenue weights of each. * The platform allows anyone to store one single source of truth on the platform by facilitating materiality threshold updates and iterating on impacts, risks, and opportunities. Note: Depending on your contract, recent updates to DMA data and new features may not appear automatically. In some cases, updates become visible only during the agreed annual update cycle. For further details, please contact your Upright representative.
### EU taxonomy * 5th of December: Disclosures for the new EU-taxonomy metrics 4.26-4.31 for nuclear and gaseous fuels, as defined in [Annex I to Delegated Regulation (EU) 2021/2139](https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32022R1214), have been made available on the platform and [via API](https://api.uprightproject.com/documentation#operation/getMetricsRegulatory). * 5th of December: Disclosures for all EU-taxonomy metrics have be updated based on 2024 sustainability reports. * Companies have ceased reporting disclosures for the objective Climate change mitigation or adaptation (TCCT), and are now reporting the union of all six objectives (TTOT). Upright ceased collecting TCCT disclosures, but continues to provide TCCT estimates. # 1.10.0 (10 / 2025) Release notes for release 1.10.0. ## Release notes 10/2025 Release number: 1.10.0\ Release date: 8th October, 2025\ Notes updated: 8th October, 2025\ Current state: `Stable` The most significant improvements to the Upright model compared to the previous release 1.9.0 are listed below. ### **Company coverage** * A set of companies has been updated based on reported 2024 revenue splits. ### CSRD double materiality assessment Incorporating the latest LLMs (large language models) into the Upright Data Engine: * In general, the Upright DMA Data Engine has been updated to use the latest LLMs published in autumn 2025, resulting in improved specificity and accuracy across product-impact pairs, impact reasonings, value chain assessment, scale/scope/irremediability/likelihood values, and ultimately materiality scores and materiality of IROs. * The update is particularly noticeable under ESRS S3 Affected Communities, where, for example, impacts now receive lower probability values overall and materiality scores therefore exceed thresholds less frequently. * Reminder: LLMs are applied only in well-defined and controllable steps within the Upright data model, and their outputs undergo multiple validation rounds, including expert reviews. Upright releases market’s first fully self service double materiality assessment solution: * Users can conduct their double materiality assessment in self service mode assisted with LLMs and in-application guidance. For example, the list of products and services will be prepopulated with an option for users’ own adjustments and iteration. New users will have access to interactive platform onboarding tours to empower a wider user base. * The new self service solution offers companies an option to get started at no-cost on Starter tier and upgrade options to increase assessment breadth and additional functionalities to fit their needs in use-cases such as sustainability reporting, audit, and strategy integration. \ Impact-specific details consolidated into one place for simpler navigation * Users can find all relevant details on a specific impact on an impact side bar. * Sidebar consolidates impact summary details, product, geographies, and disclosures that influence materiality, and scientific evidence. Note: Depending on your contract, recent updates to DMA data and new features may not appear automatically. In some cases, updates become visible only during the agreed annual update cycle. For further details, please contact your Upright representative. ### **SFDR Principal Adverse Impacts (PAI)** * More companies receive a flag for controversial weapons after a wider set of investor exclusion lists were processed. # 1.9.0 (06 / 2025) Release notes for release 1.9.0. ## Release notes 06/2025 Release number: 1.9.0\ Release date: 16th June, 2025\ Notes updated: 12th June, 2025\ Current state: `Stable` The most significant improvements to the Upright model compared to the previous release 1.8.0 are listed below. More information on releases can be found on page [Release cycle](/releases/release-cycle). ### **Company coverage** * A set of companies has been updated based on reported 2024 revenue splits. ### CSRD double materiality assessment * Thresholds are now applied at the IRO (impact, risk, opportunity) level instead of the sustainability matter level. This adjustment aligns with EFRAG recommendations and evolving market practices, ensuring that sustainability matter sizes do not disproportionately influence DMA results. * Materiality score thresholds have been recalibrated. The new thresholds are 20 for impacts and 8 for risks and opportunities. Any IRO meeting or exceeding these values is now considered material for the assessed company. * New Result tailoring tools allow for the integration of insights derived from prior DMAs or stakeholder engagement. IROs can be added or disabled directly on the Upright platform. This functionality is available upon request for customers with their latest delivery in May 2025 or later. Note: All Upright DMA customers will have the opportunity to access these updates and new features when their results are next updated in 2025. These updates are not automatically applied to previously delivered DMA results. ### **SFDR Principal Adverse Impacts (PAI)** * Multiple improvements have been implemented for SFDR PAI indicators during Q2/2025. These improvements were made available in the 1.8.0 release in early May and are documented in the respective [release notes](/releases/release-notes/1.8.0-04-2025). # 1.8.0 (04 / 2025) Release notes for release 1.8.0. ## Release notes 04/2025 Release number: 1.8.0\ Release date: 2nd April, 2025\ Notes updated: 15th May, 2025\ Current state: `Stable` The most significant improvements to the Upright model compared to the previous release 1.7.0 are listed below. More information on releases can be found on page [Release cycle](/releases/release-cycle). ### **Company coverage** * 11 000 unlisted companies have been added to the extended coverage, increasing the total [off-the-shelf company coverage](/coverage/off-the-shelf-coverage#company-coverage) to 57 000. Added companies belong to the scope of [PE funds database](https://uprightplatform.com/browse/funds?filters=%7B%22fundUniverse%22%3A%5B%22uprightDatabase%22%5D%2C%22fundType%22%3A%5B%22private-equity%22%5D%7D) [launched](https://www.uprightproject.com/blog/pe-fund-database-launch/) in December 2024. * A set of companies have been updated to the year 2024. Updates to data set recency continue during Q2 as new company annual reports are published. ### **SFDR Principal Adverse Impacts (PAI)** * PAI disclosures and estimates have been updated across metrics. * More disclosures for year 2024 are being added to latest release 1.8.0 during May and early June as companies publish 2024 sustainability reports . * A new datasource has been integrated to [UNGC/OECD norm violations](https://docs.uprightplatform.com/methodology/sfdr-pai-indicators#ungc-oecd-norm-violations) metric. Financial exclusion tracker database includes exclusion lists by 90+ financial institutions. * Coverage for GHG intensity, Non-renewable energy share and Energy consumption intensity estimates has been improved significantly. Estimates are calculated for a wider range of private companies. ### CSRD double materiality assessment * DMA database has been [launched](https://www.uprightproject.com/blog/50k-dma-launch/) increasing the off-the-shelf coverage for double materiality assessments to 50,000+ companies. * An improved materiality results summary view has been published to make it easier to find and access material sustainability matters. This includes: * A simplified view with direct visibility into all sustainability matters and their materiality. * A toggle to view or hide non-material matters. * Information about non-material matters has been made visible, providing an opportunity to review why certain matters are not considered material for a company. [Explore this demo profile for more](https://uprightplatform.com/company/ceef7aa5-daa5-41d5-9402-b7619930cefd/Sanofi/dma/materiality-results). * All DMA customers of Upright will have the opportunity to access this information when their results are updated in 2025. Non-materiality information is not automatically shown for previously delivered DMA results. # 1.7.0 (11 / 2024) Release notes for release 1.7.0. ## Release notes 11/2024 Release number: 1.7.0\ Release date: 27th November, 2024\ Notes updated: 19th March, 2024\ Current state: `Stable` The most significant improvements to the Upright model compared to the previous release 1.6.0 are listed below. More information on releases can be found on page [Release cycle](/releases/release-cycle). ### **Company and fund coverage** * **Fund coverage**: An off-the-shelf benchmark universe of 1,000+ private equity funds will be added to the Upright platform on Tuesday 3rd of December. ### **Net impact** * **Scarce human capital & jobs impacts**: To improve comparability of company results, an adjustment that fine-tunes estimated intensities for the scarce human capital and jobs impacts based on the proportion of a company's reported employee count and revenue has been removed. The change mostly affects small companies, generally lowering impact size estimates related to these impact categories and increasing the net impact ratio of small companies. {% hint style="info" %} **Removal of adjustments to scarce human capital and jobs intensity** Upright produces estimates on the jobs and scarce human capital intensity of a company primarily based on the jobs and scarce human capital intensity of their products and services, which are estimated based on OECD Structural Demographic Business Statistics (SDBS), and results from the surveys from the OECD Programme for the International Assessment of Adult Competencies (PIAAC).
The now-removed adjustment fine-tuned the results by comparing the company's reported employees-per-revenue to what we would expect a company's employees-per-revenue to be based on the company's products and the OECD SDBS, and using the difference to adjust the initial estimates. In other words, if the reported employees-per-revenue was higher than expected, the adjustment would increase the scores for both scarce human capital and jobs impacts. Conversely, it would lower the scores, if the reported employees-per-revenue was lower than expected. While the adjustment theoretically improves accuracy of results, the now-removed adjustment resulted in certain problematic dynamics. While the problems are somewhat present in all types of companies, they are best illustrated by two especially problematic examples, consultancies and startups:
**Consultancies**: Consider two management consulting firms (A and B) that provide the same products and services, and have the same amount of revenue. Company A, however, has twice as many employees as company B. Is company A's scarce human intensity twice as big as company B? No, most likely company A employs more highly skilled, i.e. more scarce human resources. Most likely, the opportunity cost of the average employee of company A is twice as big as the one of company B's average employee, meaning their scarce human intensities ought to be equal, i.e. no adjustment is needed, and any adjustment would be unfair to company B. **Startups**: In its impact data, Upright seeks to capture the size of a company's impact *relative to a company's size*. As an proxy for a company's size, we use revenue. As all proxies, it is not perfect, but works generally well for this purpose. The edge case where this proxy breaks down is startups that have no or very little revenue - there are some huge pre-revenue startups. For such companies, Upright's net impact profiles are best understood in terms of "what would the impact profile of the company be if it had revenue", or "what is the expected value of the company's impacts". The adjustment, however, would only be based on what the company's employees-per-revenue ratio is today (or when the figures were reported), which led the now-removed adjustment to overemphasise the scarce human resources intensity and jobs-intensity of startups. As a compounding problem, employee count and revenue figures are not available for many startups, leading to different treatment (and different impact profiles) of companies for which such figures are available, and the ones for which they are not. Based on the considerations above, the adjustment has been removed in release 1.7.0. {% endhint %} * In winter of 2024, Upright is focused on a longer Net impact development project with the goal of radically improving results quality and transparency. The results of the development project will be released later on. ### **SFDR Principal Adverse Impacts (PAI)** * Improved error detection for disclosed values: Error detection systems for disclosed PAI metrics have been improved and data points have been corrected accordingly. Affected metrics include the following PAI and EU taxonomy metrics: * Emissions to water * GHG emissions (Scope 1,2 ,3) * Hazardous waste * Non-renewable energy share * Total energy consumption * Water usage * EU taxonomy The disclosure corrections have also been applied to results belonging to release 1.6.0 ### **EU taxonomy** Following improvements have been included to latest 1.7.0 during December 2024 and January 2025 * New EU taxonomy company disclosures have been extracted from company reports for the year 2023 and made available on the platform and via the API. * Company disclosures are available on the level of the six objectives in addition to the union of the objectives. * Company disclosures include shares of aligned revenue (or CapEx/OpEx) from transitional and enabling activities for the union of all objectives (TTOT\_alignment\_enabling, TTOT\_alignment\_transitional, TTOT\_alignment\_capex\_enabling, etc. in the API). ### CSRD double materiality assessment * **Fund-level export**: Fund-level DMA results can be downloaded in Excel format. * **Impact reasonings**: CSRD impacts feature detailed “reasonings”, i.e. more detailed explanations that clarify the connection between a product and its specific impact. These reasonings are available in the Impact Materiality module under the “Product-Level Assessment” section. To view the reasonings, select “Show Reasonings”, which will display an additional column with the reasoning text in the table below. * **Improved risk and opportunity descriptions:** The Financial Materiality module for CSRD has been updated with clearer and more informative descriptions of risks and opportunities. These updates also specify the risk type, such as transition or physical risk, where applicable. * **New module to measure financial statement effects of risks and opportunities**: The new module enables users of the Upright Platform to translate their DMA results into monetary effects across each line item in their company’s income statement, balance sheet and cash flow statement over the short-, medium- and long-term time horizons. This enables sustainability professionals to cover phased-in CSRD requirements, CFOs to understand the effects of sustainability risks and opportunities on their bottom line, and institutional investors to manage their portfolio-level risks. All DMA customers of Upright will have a chance to upgrade their subscription to include this module when their results are updated in 2025. Please note that impact reasonings and improved risk/opportunity descriptions are included in all upcoming CSRD assessments. They are not automatically applied to previously delivered DMA results. For existing customers, these developments will be made available latest during the data updates of H1/2025. # 1.6.0 (09 / 2024) Release notes for release 1.6.0. ## Release notes 09/2024 Release number: 1.6.0\ Release date: 2nd October, 2024\ Notes updated: 2nd October 2024\ Current state: `Stable` The most significant improvements to the Upright model compared to the previous release 1.5.0 are listed below. More information on releases can be found on page [Release cycle](/releases/release-cycle). ### **Company and fund coverage** * Coverage of mutual funds and ETFs on Upright platform has been increased to 35,000, making Upright platform a comprehensive impact database for funds, besides companies. Fund database is publicly available on the [Upright platform](https://uprightplatform.com/main/?companyPreset=SP500ESG\&tab=funds\&fundPreset=bestPerformingEtfs). ### CSRD double materiality assessment * Upright’s Generalized Impact Statement Extractor — a tool based on large language models (LLMs) for analyzing scientific articles and identifying science-based impacts of products and services — has been further fine-tuned, leading to improved accuracy, particularly in ESRS sustainability matters related to affected communities, consumers and end-users, and business conduct. * The Materiality Matrix and Summary tabs will be added to the funds view on the Upright platform early October, complementing the existing impact materiality and financial materiality tabs. # 1.5.0 (06 / 2024) Release notes for release 1.5.0. ## Release notes 06/2024 Release number: 1.5.0\ Release date: 19th June, 2024\ Notes updated: 19th June, 2024\ Current state: `Stable` The most significant improvements to the Upright model compared to the previous release 1.4.0 are listed below. More information on releases can be found on page [Release cycle](/releases/release-cycle). ### **Company and fund coverage** * The automated methods to extract company product mix information from company reporting and websites have been updated to utilize the most recent improvements in LLM technologies. The new product mixes more thoroughly cover the whole range of company business. The improvements affect 25 000 companies in the extended coverage * Coverage of mutual funds and ETFs on Upright platform is being gradually ramped up to \~45,000 during June, making Upright platform a comprehensive impact database for funds, besides companies * Revenue and employee count data based on year 2023 disclosures have been received from data provider Demandbase updated to impact calculations ### **Net impact** * The modeling of products and services has been improved by adding value chain links, broadening the range of product phrases catching scientific articles and adding completely new products to the product taxonomy ### **EU taxonomy &** SFDR Principal adverse impacts * New EU taxonomy and EU SFDR PAI indicator company disclosures have been extracted from company reports for the year 2023 and made available on the platform and via API * Significant coverage increase for EU SFDR PAI indicators from previous reporting years, especially for total energy consumption and non-renewable energy share * Taxonomy eligibilities and taxonomy alignments have been refined for a set of 110 highly granular products ### CSRD double materiality assessment * A new list of impacts -view has been added to the Upright platform, complementing the existing CSRD product-level view. This new view enables users to access a summarized list of all impacts related to a specific sustainability matter, which supports the undertaking in IRO reporting. * 20+ new third-party datasets sourced from reputable, well-established organizations have been incorporated to enhance the comprehensiveness of CSRD assessments. These datasets include, for instance, data from the [Corruption Perception Index ](https://www.transparency.org/en/cpi/2023?gad_source=1\&gclid=Cj0KCQjw9vqyBhCKARIsAIIcLMGYj9686hWcCHFrisoV-QN3k367cHzSi71_umB1loaXetEZMoYeos4aAl69EALw_wcB)2023 by Transparency International, SDG Labour Market Indicator by [ILOSTAT](https://ilostat.ilo.org/) and [List of Goods Produced by Child Labor or Forced Labor](https://www.dol.gov/agencies/ilab/reports/child-labor/list-of-goods) by United States Department of Labor # 1.4.0 (03 / 2024) Release notes for release 1.4.0. ## Release notes 03/2024 Release number: 1.4.0\ Release date: 20th March, 2024\ Notes updated: 20th March, 2024\ Current state: `Stable` The most significant improvements to the Upright model compared to the previous release 1.3.0 are listed below. More information on releases can be found on page [Release cycle](/releases/release-cycle). ### **Net impact** * [Net impact sum](/metrics/net-impact#net-impact-sum-nis) has been added as a secondary aggregate metric representing net impact in addition to Net impact ratio. Net impact sum is calculated by subtracting the total sum of negative impact, or costs, the company creates, from the sum of positive impacts * Input data for the value chain model has been updated by integrating an input-output dataset [USEEIOv2](https://www.epa.gov/land-research/us-environmentally-extended-input-output-useeio-technical-content). Value chain model now more accurately captures factors such as transportation, telecommunications and energy use across all industries * The modeling of the pulp and paper industry and related value chains has been updated by granularization of the downstream uses of paper products. Biodiversity impacts of logging and energy consumption in pulp and paper production have been calibrated to follow latest scientific research * The allocation of impact from enabled industries to investing and lending has been systematized * Negative environmental impacts for biofuels and biomass, especially GHG, non-GHG and biodiversity impacts, have increased due to the higher number of scientific articles contributing to the results * The precision of value flow information for energy power plant construction, operation, maintenance and modernisation, as well as the electricity products, has been improved. This has increased the value flow between these products, consequently narrowing the gaps in results: for example, power plant construction products now pass on more negative environmental impacts to the operational phase, resulting in a closer alignment of results between these stages * The positive impacts of wastewater management and hazardous and non-hazardous waste treatment are now explicitly reflected not only in the impact category waste but also in the broader context of societal infrastructure. This update has increased the overall net impact for these products * Mined elements, such as base metals and precious metals, have heightened their environmental impacts as the model captures an increasing number of scientific articles discussing the adverse effects on emissions and the harm to biodiversity caused by mining ### **EU taxonomy** * EU taxonomy metrics have been updated to align with the most recent criteria set by the Commission in the Taxonomy Environmental Delegated Act and Climate Delegated Act amendments [published](https://finance.ec.europa.eu/publications/sustainable-finance-package-2023_en) in November 2023. The changes apply to reporting as of January 2024 * Updates concern all objectives. The most significant alterations are in the eligibility of non-climate environmental objectives: * Sustainable use of water and marine resources * Transition to circular economy * Pollution prevention and control * Protection and restoration of biodiversity and ecosystems * The numbering and naming of activities have been updated to match the contents of the Taxonomy Delegated Acts * Fund and company group level EU-taxonomy metrics have been updated to exclude sovereign bonds, commodities, non-companies and business units
# 1.3.0 (12 / 2023) Release notes for release 1.3.0. ## Release notes 12/2023 Release number: 1.3.0\ Release date: 5th December, 2023\ Notes updated: 5th December, 2023\ Current state: `Stable` The most significant improvements to the Upright model compared to the previous release 1.2.0 are listed below. More information on releases can be found on page [Release cycle](/releases/release-cycle). ### **Company coverage** * Upright [off-the-shelf company coverage](https://docs.uprightplatform.com/coverage/off-the-shelf-coverage) has been increased to 50,000, mostly for the coverage class extended * Products view on the Company page has products organized under operating segments for companies with disclosed segments. Upright is working on increasing the number of companies with segments available ### **Net impact** * Results within the *Non-GHG emissions* impact category have been refined to better incorporate research findings related to factors such as chemicalization, persistent chemical compounds, inorganic and organic point source pollution, and microplastics. This improvement has resulted in *Non-GHG emissio*n result changes across industries, particularly affecting companies operating in the energy and transportation sector * [International labour statistics](https://ilostat.ilo.org/) related to gender diversity and workforce nationality and U.S. department of labour [list of Goods Produced by Child Labor or Forced Labor](https://www.dol.gov/agencies/ilab/reports/child-labor/list-of-goods) have been integrated into the *Equality and human rights* impact category ### UN SDG alignment * UN SDG alignment summary aggregates with split to E/S dimensions are visible in a table in the SDG lens and available via API ### **EU taxonomy & SFDR Principal adverse impacts** * Year is now available for Principal Adverse Impact indicators and EU taxonomy estimates and disclosures on the Upright platform. Previously the year has been available via API only * New EU taxonomy and EU SFDR PAI indicator company disclosures have been extracted from company reports published during H2/2023 and made available on the platform and via API
# 1.2.0 (09 / 2023) Release notes for release 1.2.0. ## Release notes 09/2023 Release number: 1.2.0\ Release date: 27th September, 2023\ Notes updated: 5th December, 2023\ Current state: `Stable` The most significant improvements to the Upright model compared to the previous release 1.1.0 are listed below. More information on releases can be found on page [Release cycle](/releases/release-cycle). ### **Net impact** * Value chain links between different energy industry products (e.g. renovation, operation, maintenance, modernization, and construction of energy power plants) have been improved. As a result, negative environmental impacts created in the construction and operation phase are now better shown in other energy products’ impact scores * Impact differences between leasing, rental and ownership have been researched, and products related to rental and leasing of industrial equipment, electronics, furniture, clothing and vehicles have been systematized. This has resulted in various score changes for rental and leasing products * Retail and consumer goods’ value chains have been improved and systematized to better reflect the impacts accumulating from the production and distribution phase. This has increased the negative environmental and health impacts allocated to consumer goods and retail of consumer goods * Retail’s contribution to societal infrastructure in the form of supplying critical goods and ensuring their availability to end-users has been reassessed and is now better internalized in the retailers’ impact profiles * Improved value chain information has been introduced to restaurant and café services. This has increased the negative environmental impacts allocated to restaurant meals containing meat and increased the negative waste impacts of all restaurant services * An issue causing part of company products being ignored in impact calculation has been addressed. For less than 2% of companies in Upright coverage one or multiple products were ignored in the result calculation. The issue occurred for some time in release latest 1.1.0 and has been addressed ### UN SDG alignment * UN SDG alignment data now includes the pure revenue shares for each alignment level (strong, moderate, and weak). These simply reflect the share of revenue from products that are weakly, moderately, or strongly aligned * On the Upright Platform, the pure revenue shares are available within the UN SDG Alignment section. They can be viewed by selecting ‘Pure revenue shares’ in the dropdown titled "View mode" * The pure revenue shares are also available via the Upright API. In previous releases, pure revenue shares were only available for strong alignment. Note that previous releases included a field called "SDG8\_alignment\_moderate", but this did not reflect a pure revenue share * "Total alignment" has been re-titled as "Summary percentage", to better represent its nature of a weighted average of pure revenue shares from different alignment levels * Product mappings have been improved to catch the alignment and misalignment of individual products in a more detailed manner. Development work has been focusing on infrastructure, materials and other natural resource-intensive products and it affects primarily alignments towards SDGs 9, 11, and 12 * An issue causing part of company products being ignored in SDG alignment calculation has been addressed. For less than 2% of companies in Upright coverage one or multiple products were ignored in the result calculation. The issue occurred for some time in release latest 1.1.0 and has been addressed ### **EU taxonomy & SFDR Principal adverse impacts** * New EU taxonomy and EU SFDR PAI GHG indicator company disclosures have been extracted from company reports published during Q2/2023 and Q3/2023 and made available on the platform and via API * An issue causing some of company-disclosed EU taxonomy data to be missing has been addressed. The issue occurred in release latest 1.1.0 between June 14th and August 19th * A table containing PAI values for fund and portfolio constituents has been added under the EU SFDR PAIs lens of funds and portfolios on the Upright platform * An issue causing part of company products being ignored in EU taxonomy and SFDR PAI estimate calculation has been addressed. For less than 2% of companies in Upright coverage one or multiple products were ignored in the result calculation. The issue occurred for some time in release latest 1.1.0 and has been addressed # 1.1.0 (06 / 2023) Release notes for release 1.1.0. ## Release notes 06/2023 Release number: 1.1.0\ Release date: 14th June, 2023\ Notes updated: 27th September, 2023\ Current state: `Stable` The most significant improvements to the Upright model compared to the previous release 1.0.0 are listed below. More information on releases can be found on page [Release cycle](/releases/release-cycle). ### **Net impact** * Methodology for modeling corporate loans has been updated. The impact of companies providing corporate loans now resembles more closely the target industries they enable * The target industry split of corporate loans for individual banks is calculated using disclosed data from the banks whenever available. In situations where disclosed data is not available, an estimated average corporate loans split by industry is used. Average split has been estimated based on various sources such as [Banking on Climate Chaos](https://www.bankingonclimatechaos.org/) * The value flows between recycled raw materials and products using these materials have been improved. In addition, for calculating allocation of impacts from virgin to recycled raw materials, the loss of quality method used in many LCA calculation methodologies was extended to cover all recycled materials. These changes have slightly decreased the positive environmental impacts of many secondary raw materials, and added some new downstream impacts to them ### **EU taxonomy & SFDR Principal adverse impacts** * Year is now available for Principal Adverse Impact estimates via the Upright API. Previously this was available only for disclosed values * Company-disclosed values for the following EU taxonomy indicators now available: * EU taxonomy alignment (turnover, CapEx, OpEx) * EU taxonomy eligibility (turnover, CapEx, OpEx) * (Note: turnover-based company-disclosed EU taxonomy eligibility figures were available already previously. The others are additions.) * Company-reporting-based values are now available for the following optional PAI indicators: * Lack of human rights policy * Lack of anti-corruption/anti-bribery policy * Water usage * Given that companies never directly state a lack of a human rights or anti-corruption/anti-bribery policy, Upright infers a lack of such policy if the company's annual reports and/or sustainability reports do not state the existence of such a policy. As this is not a direct disclosure, these are marked as Upright modelled estimates, despite them being based on company reporting * Overall, coverage of disclosed figures has increased considerably, especially for indicators related to EU SFDR PAI GHG emissions and the EU taxonomy * EU SFDR PAI indicator estimates for GHG emissions have been updated using a broader set of company disclosure data points and enhanced estimation algorithm to provide more accurate estimates * The modelling has been improved especially for companies producing software applications, digital cloud platforms, engineering and other professional services * EU SFDR PAI indicator estimates for other PAI indicators have been been updated using a broader set of company disclosure data point to provide more accurate estimates ### API changes * Breaking change: a new API query parameter “scoreUnit” has been introduced to control which unit any queried impact scores are returned in. To avoid API users from accidentally receiving other than the expected units, the “scoreUnit” parameter will become mandatory in June 14th. The Upright’s classic score will remain available for reporting and other purposes through both API and Upright Platform * The “scoreUnit” parameter accepts one of the three following values: * **cents-per-dollar**: The returned scores describe how many cents of cost / benefit the assessable produces for each dollar of revenue * **dollars-per-year**: The returned scores describe how many dollars of cost / benefit the assessable produces every year with its revenue. Available only for companies, not on the aggregate level (e.g. fund or index) * **classic**: The returned scores use the same scale as the Upright platform has been using until the 1.0.0 release * The “scoreUnit” parameter is supported by the following API end-points: * [GET /metrics/all](https://api.uprightproject.com/documentation#operation/getMetricsAll) * [GET /metrics/basic](https://api.uprightproject.com/documentation#operation/getMetricsBasic) * [GET /metrics/extended](https://api.uprightproject.com/documentation#operation/getMetricsExtended) * [GET /profile](https://api.uprightproject.com/documentation#tag/Net-Impact-Profile) {% hint style="warning" %} Breaking change: a new API query parameter `scoreUnit`has been introduced to control which unit any queried impact scores are returned in. The `scoreUnit`parameter becomes mandatory in June 14th. {% endhint %} # 1.0.0 (04 / 2023) Release notes for release 1.0.0. ## Release notes 04/2023 Release number: 1.0.0\ Release date: 21st April, 2023\ Notes updated: 14th June, 2023\ Current state: `Stable` The most significant improvements to the Upright model compared to the previous release 0.8.0 are listed below. More information on releases can be found on page [Release cycle](/releases/release-cycle). ### **Company coverage** * Upright is increasing its public platform coverage to 10,000+ profiles based on companies' public information. Also Upright’s [off-the-shelf company coverage](https://docs.uprightplatform.com/coverage/off-the-shelf-coverage) for subscribed users has been increased ### **Net impact** * Impact cents per dollar has been introduced as the new default unit of measure of impact scores. Previously used relative scores remain available for the users on platform and via API. Additionally annual dollars have been made available as a new measure of impact * Impact specific units, like CO2 equivalent tons and disability-adjusted life years (DALYs), have been introduced to help correlate monetary impact values with outcomes in each impact category * Sector level value flows have been re-calibrated by incorporating millions of new value chain links based on various macroeconomic databases such as the [OECD ICIO](http://oe.cd/icio) database and [IEA electricity consumption](https://www.iea.org/data-and-statistics/charts/world-electricity-final-consumption-by-sector-1974-2019) statistics * The value chain distribution of impact has been more closely aligned with established value chain definitions: * Upstream impacts cover attributed impacts created by a company’s direct and indirect suppliers * Internal impacts cover impacts directly created by the company’s own activities * Downstream covers product end-use and disposal, as well as a attributed impact of direct and indirect customers of the company * Default economic costs and benefits of impacts have been reassessed based on the latest academic research and findings. This affects the relative sizes of individual impact categories. Read more about economic costs and benefits factors, and impact monetization methodology [here](/methodology/net-impact/weighting-of-impacts) * The most significant score increases appear in the following impact categories: Biodiversity+, Biodiversity-, Physical diseases-, Mental diseases-, Nutrition+, and Societal infrastructure+ * The most significant score decreases appear in the following impact categories: Mental diseases+, Relationships-, Jobs+, and Scarce human capital- ### **EU taxonomy & SFDR Principal adverse impacts** * EU SFDR PAI indicator estimates for Scope 1, 2 and 3 GHG emissions and Total energy consumption have been updated using a broader set of company disclosure data points to provide more accurate estimates ### API changes * In line with with the new monetary units for net impact data, a new API query parameter `scoreUnit` has been introduced to control which unit any queried impact scores are returned in * This `scoreUnit` parameter is supported by the following end-points: * [GET /metrics/all](https://api.uprightproject.com/documentation#operation/getMetricsAll) * [GET /metrics/basic](https://api.uprightproject.com/documentation#operation/getMetricsBasic) * [GET /metrics/extended](https://api.uprightproject.com/documentation#operation/getMetricsExtended) * The `scoreUnit` parameter accepts one of the three following values: * `classic` (default until 1.0.0): The returned scores use same scale as the Upright platform has been using until 1.0.0 release * `cents-per-dollar` (default from release 1.1.0 onwards): The returned scores describe how many cents of cost / benefit the assessable produces for each dollar of its total revenue * `dollars-per-year`: The returned scores describe how many dollars of cost / benefit the assessable produces every year with its revenue {% hint style="warning" %} Upcoming breaking change: the default value of the `scoreUnit` parameter will change from `classic` to `cents-per-dollar` in release 1.1.0 (scheduled for June). Specifying `scoreUnit` before this change is recommended for all API integrations to ensure consistent response data. Further information will be provided prior to the change. {% endhint %} # 0.8.0 (03 / 2023) Release notes for release 0.8.0. ## Release notes 03/2023 Release number: 0.8.0\ Release date: 15th March, 2023\ Notes updated: 21st April, 2023\ Current state: `Stable` The most significant improvements to the Upright model compared to the previous release 0.7.100 are listed below. More information on releases can be found on page [Release cycle](/releases/release-cycle). ### **Net impact** * More research (see e.g. [1](https://bjsm.bmj.com/content/50/20/1252.short), [2](https://journals.sagepub.com/doi/abs/10.1177/1745691615592234?journalCode=ppsa)) on negative mental health impacts of digital games has been integrated into the Upright model. This has increased the negative mental health impact for digital games and related products * The negative physical diseases impact of digital games and other sedentary activities have been calibrated against recent research (see e.g. this [meta analysis](https://www.sciencedirect.com/science/article/abs/pii/S0277953619302941)). This has decreased the negative physical health impact from sedentary activities * More detailed value chain information has been introduced to biofuel and biomass products, better taking into account the raw material sources per each biofuel or biomass type. This has decreased the negative environmental impacts for several products * Improved value chain information has been introduced to Management consulting services. The impacts of downstream industries now show more systematically in the impact profiles of management consulting services * The negative environmental impacts of home nursing care services have been calibrated to better take into account the emissions caused by driving * Portfolio level aggregation of Net impact data has been changed. The change does not affect portfolio net impact ratios. After the change the weight of unrecognized assets is not considered in the denominator in portfolio level aggregation. Unrecognized assets may include, for example, currencies, government bonds and unrecognized company bonds. In previous releases unrecognized assets were considered to have zero impact, while from 0.8.0 onwards they will be assumed to have the average impact of recognized assets. This change will come to effect in release 0.8.0 by 26th March * As a general note, Upright continues to devote significant resources to the development of the model, including improvements in product taxonomy, value flows and broadening the range of product phrases catching scientific articles ### **UN SDG alignment** * Portfolio level aggregation of UN SDG alignment data does not the weight of unrecognized assets is not considered in the denominator in portfolio level aggregation. See more detailed information under Net impact ### **EU taxonomy & SFDR Principal adverse impacts** * EU SFDR PAI indicator estimates for GHG and Non-renewable energy have been updated using a broader set of company disclosure data points to provide more accurate estimates * EU Taxonomy alignment estimates have been enhanced to follow more closely the latest [annex](https://finance.ec.europa.eu/system/files/2022-03/220330-sustainable-finance-platform-finance-report-remaining-environmental-objectives-taxonomy-annex_en.pdf)[ to the report on preliminary recommendations for technical screening criteria for the EU taxonomy](https://finance.ec.europa.eu/system/files/2022-03/220330-sustainable-finance-platform-finance-report-remaining-environmental-objectives-taxonomy-annex_en.pdf) from the Platform on Sustainable Finance # 0.7.100 (01 / 2023) Release notes for release 0.7.100. ## Release notes 01/2023 Release number: 0.7.100\ Release date: 31st January, 2023\ Notes updated: 15th March, 2023\ Current state: `Stable` The improvements to the Upright model compared to the previous release 0.7.0 are listed below. More information on releases can be found on page [Release cycle](/releases/release-cycle). ### **EU taxonomy & SFDR Principal adverse impacts** * Based on [annex to the report on preliminary recommendations for technical screening criteria for the EU taxonomy ](https://finance.ec.europa.eu/publications/call-feedback-platform-sustainable-finance-preliminary-recommendations-technical-screening-criteria_en)from the Platform on Sustainable Finance, Upright’s EU taxonomy alignment estimates have been enhanced to better account for Do No Significant Harm criteria * New EU taxonomy and EU SFDR PAI GHG indicator company disclosures have been extracted and made available on the platform and via API * 13th March 2023: EU Non-Financial Reporting Directive reporting obligation estimates for companies have been made available via API. Upright [methodology](https://docs.uprightplatform.com/appendix/nfrd-status-metadata) for estimating the NFRD reporting obligation follows closely both EU regulation and national legislations * 13th March 2023: Upright’s EU taxonomy alignment data has been broadened with alignment estimates for nuclear energy and fossil gaseous fuels related activities. The new metrics are available via the [API](https://api.uprightproject.com/documentation#operation/getMetricsRegulatory) and on the EU taxonomy alignment table on the Upright Platform * 13th March 2023: New EU SFDR PAI indicators for GHG intensity and societal violations of nations have been made available via the [API](https://api.uprightproject.com/documentation#operation/getMetricsRegulatoryCountries) * 13th March 2023: An issue causing inconsistent EU taxonomy alignment estimates of the union objective has been addressed. For a small portion of companies, the EU alignment figures that combine multiple objectives and activity types were underestimated and inconsistent with individual objective figures. The issue occurred briefly in release 0.7.100 and has been addressed # 0.7.0 (12 / 2022) Release notes for release 0.7.0. ## Release notes 12/2022 Release number: 0.7.0\ Release date: 8th December, 2022\ Notes updated: 27th January, 2022\ Current state: `Stable` The most significant improvements to the Upright model compared to the previous release 0.6.0 are listed below. More information on releases can be found on page [Release cycle](/releases/release-cycle). ### **Net impact** * [Loss of quality method](https://dspace.mit.edu/handle/1721.1/60047) used in many LCA calculation principles has been introduced for calculating allocation of impacts from virgin materials to secondary raw materials. This has decreased the negative environmental impacts of many secondary raw materials * The value flow information for primary and secondary batteries (e.g. alkaline and lithium-ion batteries) has been made more precise. This has increased the social infrastructure impact for all batteries and positive environmental impacts for secondary batteries which are used for electric low-carbon vehicles * GHG and waste impacts related to consumerism have been better allocated to marketing services and software, increasing these negative environmental impacts * Improved value chain information has been introduced to restaurant, bar and café services. This has decreased the negative health impacts allocated to serving alcohol in these venues * Downstream value chain information for packaging materials has been improved: the share of packaging allocated to alcoholic beverages is now smaller, resulting in less negative health impacts for drink packaging * Positive environmental impacts for the repair, maintenance and rental of industrial equipment have been slightly decreased due to the increased number of scientific articles contributing to the results * More granular nutritional value data has been added for processed food products to increase the accuracy of Nutrition impact * Additional data sources for clothing greenhouse gas emissions have been introduced resulting in changes in the negative GHG impacts of clothing products * The environmental benefits of industrial enzymes have been increased due to increased number of scientific articles contributing to the results * As a general note, Upright continues to devote significant resources to the development of the model, including improvements in product taxonomy, value flows and broadening the range of product phrases catching scientific articles ### **EU taxonomy & SFDR Principal adverse impacts** * Based on the [final report](https://finance.ec.europa.eu/system/files/2022-04/220330-sustainable-finance-platform-finance-report-remaining-environmental-objectives-taxonomy_en.pdf) with recommendations from the Platform on Sustainable Finance published on 30th March 2022, Upright’s EU taxonomy alignment data has been broadened to also cover the remaining four environmental objectives under the EU taxonomy. These objectives are: * Sustainable use of water and marine resources * Transition to circular economy * Pollution prevention and control * Protection and restoration of biodiversity and ecosystems * Hundreds of new EU taxonomy and EU SFDR PAI indicator company disclosures have been extracted and made available on the platform and via API * EU SFDR PAI indicator estimates for Board Gender Diversity and Unadjusted Gender Pay Gap have been updated using a broader set of company disclosure data points to provide more accurate estimates * Disclosure year has been added to disclosed EU taxonomy and PAI indicator data from release stable 0.6.0 onwards * Flags indicating whether EU taxonomy values are disclosed or estimated have been added to the platform and API from release stable 0.6.0 onwards * A bug incorrectly flagging PAI estimates as disclosures in the API has been fixed. The bug affected only a limited number of companies and mainly their PAI indicator Board gender diversity. The fix has been applied from release 0.6.0 onwards to maintain reproducibility of results in previous stable releases # 0.6.0 (10 / 2022) Release notes for release 0.6.0. ## Release notes 10/2022 Release number: 0.6.0\ Release date: 12th October, 2022\ Notes updated: 5th December, 2022\ Current state: `Stable` The most significant improvements to the Upright model compared to the previous stable release 0.5.0 are listed below. More information on releases can be found on page [Release cycle](/releases/release-cycle). ### **Company coverage** * 2000 new companies have been added to Upright's company coverage ### **Net impact** * Impacts that the private sector helps cause indirectly via non-marketable phenomena (e.g., sports or tourism) have been expanded in scope and calibrated against the volume of relevant research * In the societal infrastructure impact, an additional data source has been introduced: information on what is considered “critical infrastructure” according to different governmental organisations (e.g. by Finnish, US and Canadian governments). In addition, due to improvement product phrases more scientific research related to physiological basic needs of people is considered * Allocation of impacts via the value chain has been improved to consider even very niche products from which impact should be inherited * Value chain links between packaged products and packaging materials have been made more systematic. This has increased negative waste impacts for packaged products (e.g. for processed food and beverage products in disposable packaging) * As a general note, Upright continues to devote significant resources to the development of the model, including improvements in product taxonomy, value flows and broadening the range of product phrases catching scientific articles ### **UN SDG alignment** * SDG 8 (Decent work and economic growth): Product mappings have been improved to follow the target descriptions more precisely. Cryptocurrencies and gaming related products no longer get alignment as they do not contribute significantly to “... higher levels of economic productivity” as described under target 8.2 * Product mappings have been made more detailed for the transport and energy production sectors, with a focus on infrastructure. Improvements to the product mappings affect primarily alignments towards SDGs 9 and 11 * More systematic decision criteria has been developed for SDG alignment on a target level. Implementing the new criteria has increased the quality of product mappings and led to minor improvements in (mis)alignments across all SDGs ### **EU taxonomy & SFDR Principal adverse impacts** * Based on the final report with recommendations from the Platform on Sustainable Finance published on 30th March 2022, Upright’s eligibility data has been broadened to cover also the last four environmental objectives under the EU Taxonomy. Alignment data for the new objectives will be released during Q4/2022 * The specific nuclear and gas energy activities as published in the Complementary Climate Delegated Act on the 30th March 2022 has been included in the EU Taxonomy eligibility and alignment data * A breakdown of eligibility and alignment data on the enabling/transitional activity types has been added to the EU taxonomy data * PAI Indicator estimates for Non-Renewable Energy Share and Hazardous Waste have been updated: * New disclosed PAI indicator values have been extracted from annual and sustainability reports and made available on the platform * Accuracy of PAI indicator estimates have been improved using the newly extracted disclosure data * 5th December 2022: Disclosure year has been added to disclosed EU taxonomy and PAI indicator data * 5th December 2022: Flags indicating whether EU taxonomy values are disclosed or estimated have been added to the platform and API * 5th December 2022: A bug incorrectly flagging PAI estimates as disclosures in the API has been fixed. The bug affected only a limited number of companies and mainly their PAI indicator Board gender diversity. To maintain reproducibility of results, fix has not been applied to previous stable releases 0.5.0, 0.4.0, 0.3.693 # 0.5.0 (06 / 2022) Release notes for Stable release 0.5.0. ## Release notes 06/2022 Release number: 0.5.0\ Release date: 15th June, 2022\ Notes updated: 10th October, 2022\ Current state: `Stable` The most significant improvements to the Upright model compared to the previous stable release 0.4.0 are listed below. More information on releases can be found on page [Release cycle](/releases/release-cycle). ### **Company coverage** * Coverage has been increased with over 1100 new companies, most of which are U.S. listed companies ### **Net impact** * Criticality levels of different raw materials listed by the EU have been integrated into the Scarce natural resources impact * Modelling of wholesaling and retailing of different products has been updated: impacts of wholesale and retail companies are now more consistent with the impacts of products being sold * Biodiversity impact now also includes habitat loss on a smaller scale and biodiversity loss related to climate change. In addition, some new data sources related to endangered species have been integrated into the Upright model * As a general note, Upright continues to devote significant resources to the development of the model, including improvements in product taxonomy, value flows and broadening the range of product phrases catching scientific articles ### **Company insights & Company targets** * Upright Platform now includes two new lenses Company insights & Company targets. This addition complements the digital impact profile on the Upright platform, where all relevant impact-related data on companies is brought together in a unified and comparable format. At first, this data is available only for select companies ### **UN SDG alignment** * Contribution to SDG targets (subgoals) is available at the platform providing a more granular view of the SDG data. The drilldown to the target level can be accessed by clicking on any SDG alignment figure * SDG 9 (Industry, innovation and infrastructure): Financial products for consumers, such as payment cards, ATM machines, consumer deposit accounts and consumer credit granting, are no longer aligned with SDG 9. Since the target 9.3 is focused on providing financial services to enterprises, consumer products’ alignment is not justified ### **EU taxonomy & SFDR Principal adverse impacts** * New disclosures have been added to the Upright Platform based on the analysis of over 12,000 annual, sustainability and other public reports from companies # 0.4.0 (03 / 2022) Release notes for Stable release 0.4.0 ## Release notes 03/2022 Release number: 0.4.0\ Release date: 30th March, 2022\ Notes updated: 10th October, 2022\ Current state: `Stable` The most significant improvements to the Upright model compared to the previous stable release 0.3.693 are listed below. More information on releases can be found on page [Release cycle](/releases/release-cycle). ### **Net impact** * Mappings of food products with nutritional values data has been made more detailed to provide more precise impacts in the Nutrition category * As a general note, Upright continues to devote significant resources to the development of the model, including improvements in product taxonomy, value flows and broadening the range of product phrases catching scientific articles ### **UN SDG alignment** * Product mappings have been made more detailed in all SDGs. Improvements have specifically been focused on SDG misalignments and SDG 8 data (Decent work and economic growth) ### **EU taxonomy & SFDR Principal adverse impacts** * Six new SFDR PAI indicators have been released. These indicators include Activities negatively affecting biodiversity-sensitive areas, Energy consumption intensity per high impact climate sector, Emissions to water, Hazardous waste, Unadjusted gender pay gap and Manufacture of chemicals * SFDR PAI indicators for Scope 1, 2 and 3 GHG emissions have been updated based on expanded data set of company disclosures # Overview An AI chat assistant for exploring Upright's company impact data in natural language. Upright Agent is an AI assistant built into the [Upright Platform](https://uprightplatform.com), where it appears as **Ask AI**. It lets you ask questions in plain English (or your preferred language) about a company's net impact, CSRD double materiality, financial effects, SDG alignment, EU Taxonomy alignment, SFDR Principal Adverse Impacts, and the supporting scientific evidence — and it answers from the same data the rest of the platform uses. Every answer links back to the evidence behind it, so you can interrogate, verify, and defend it. The chat is intended to complement the visual platform, not replace it. Charts, tables, and result cards are still the primary way to explore the data; the chat is best at answering specific questions, summarising results across multiple companies or topics, and pulling together explanations that would otherwise require clicking through several views. ## Where to find it The chat is available in two places: * As a side panel on every company, group, and portfolio page (click **Ask AI** in the top navigation). When opened from a specific page, the chat carries that page's context so you can ask "summarise this" or "what is driving this risk" without naming the company. * As a standalone view at [`uprightplatform.com/chat`](https://uprightplatform.com/chat), where you can start an open-ended conversation that is not tied to any single page. Both surfaces share the same conversation history, so you can switch between them mid-thread. ## Who can use it The chat is available to logged-in Upright Platform users whose organization has the chat feature enabled — for many account types it is on by default. If you do not see **Ask AI** in the top navigation, contact your Upright contact person to have it enabled for your organization. ## What the chat is good at * **Single-company summaries** — "Show and summarise the DMA results for Storebrand." * **Peer comparisons** — "Compare the net impact of Apple, Microsoft, and Alphabet." * **Drill-downs** — "What is driving Climate change mitigation as a material topic for Neste?" * **Supporting evidence** — "Why is wind power assessed as positive for GHG emissions?" * **Portfolio and group views** — "Summarise the DMA across the constituents of this portfolio." * **Methodology questions** — "How does Upright weight impact categories?" ## What makes the answers trustworthy Because every answer is built on a structured tool call against Upright's own model rather than generated freely, the chat gives you guarantees you should expect from an analytical tool: * **Grounded in Upright's data.** Every number comes from Upright's own scientifically-backed model — not from a language model's own guess. Answers reflect scientific consensus, not "internet consensus". * **Reproducible.** The same question returns the same answer regardless of how you phrase it, because the figures come from the model rather than being generated on the fly. * **Coherent and honest.** Figures sum consistently to their totals, and the chat will not invent flattering numbers or fill a gap it has no data for — it says so instead. * **Traceable.** Every claim links back to the underlying tool result and the scientific evidence behind it, so you can verify and defend an answer rather than take it on trust. ## What the chat is not The chat is not a free-form general-purpose assistant. It is scoped to Upright's data and methodology. It will not write code, draft unrelated marketing copy, or answer questions outside the impact / sustainability domain — instead it will redirect you back to topics it can help with. It also does not estimate numbers it does not have. If Upright does not currently produce a particular lens for a given company (for example, EU Taxonomy alignment for a company whose primary activities are not eligible), the chat will say so rather than invent a value. See [Limits and confidentiality](/upright-agent/limits-and-confidentiality) for the full list of constraints. ## Next steps * Read [Using the chat](/upright-agent/using-the-chat) for entry points, prompt shapes, and how citations, KPI tiles, and tables work. * Read [Data and tools available](/upright-agent/data-and-tools) to see exactly which data the chat can reach. * Read [Connecting external AI clients](/upright-agent/external-clients-mcp) if you want to access the same tools from Claude Desktop, Claude.ai, Microsoft Copilot Studio, or another MCP-compatible client. (Connector access is granted separately from regular platform access.) # Using the chat How to ask questions, what the responses contain, and how follow-ups work. ## Starting a conversation There are three ways to open the chat: * **From a company, group, or portfolio page** — click **Ask AI** in the top navigation. The chat opens as a side panel and inherits the context of the page you are on. A small chip at the top of the chat shows what context the chat is anchored to (for example, "Double materiality · Storebrand"). Questions like "summarise this" or "what's driving this" resolve against that context automatically. * **From the standalone chat view** — go to [`uprightplatform.com/chat`](https://uprightplatform.com/chat) for an open-ended conversation that is not tied to any particular page. Use this when you want to compare multiple companies or ask methodology questions. * **From a welcome suggestion** — the empty chat view shows a few suggested prompts (for example, "Net impact of Apple", "DMA results for Nordea"). Click one to start the conversation with that question pre-filled. ## Attaching files and searching the web The composer has two optional inputs next to the message box: * **Add files** — attach a document (for example, a PDF) and ask the chat to read it alongside Upright's data. A common use is to hand the chat your own materiality assessment, sustainability report, or stakeholder-engagement findings and ask it to compare them against Upright's modelled results — a "what am I missing?" gap analysis. An attached file is available on the turn you attach it; if you want to keep working with it several messages later, attach it again. Uploaded files are session context only — they are not stored after the conversation and never used to train the model or shown to other organizations. * **Web search** — lets the chat pull in external context from the public web (for example, recent controversies, news, or background on a company) alongside Upright's own data. It is on by default, but the chat still prefers Upright's vetted data and will not reach out to the web unless your question asks it to. To bring in external or peer information, say so explicitly — for example, *"compare Upright's data with what public sources say"* or *"check recent controversies on the web."* Web-sourced claims are cited like any other source. This deliberate bias toward Upright's own data is what keeps answers reproducible; the web is there when you ask for it. ## What a good question looks like The chat works best when the question is specific about which company / group / portfolio you mean and which analytical lens you want. **Good examples:** * "Show and summarise the net impact of Storebrand." * "Compare the DMA results for Apple, Microsoft, and Alphabet." * "What products are driving Health and safety as a material topic for Neste?" * "List the constituents of this portfolio sorted by net impact." For private or lesser-known companies, give the website (or ISIN / ticker) — names recur across industries, so the site address is the most reliable identifier. It is worth glancing at the company the chat resolved to before you trust the answer. ### Question types that work well These are the kinds of task the chat is built for. The phrasing is illustrative — substitute the company, fund, list, or topic you care about. * **Single-metric lookup with the "why".** "What is this carmaker's GHG-emissions impact, and which products drive it?" — the chat returns the figure and can drill into the positive and negative contributors and the per-product reasoning behind them. * **Peer / industry benchmarking.** "How does this shipping company's net-impact ratio compare to its peers, and where does it rank?" — the chat builds a peer set and returns a ranked comparison. * **Screening a list on a theme.** "Screen these companies for biodiversity impact" or "which EU companies are most SDG-aligned?" — paste a short list, or ask for a ranked screen. * **Investor due diligence.** "Give a 360° view of this company for an Article 9 portfolio screen — net impact, the main risks, and any controversies." — combine Upright's lenses with the **Web search** toggle for external context. * **Compare your own report against Upright.** Attach your own materiality assessment with **Add files** and ask the chat to compare it against Upright's modelled double materiality — where the two converge and where they diverge. * **Site-level physical climate risk.** "Assess the physical climate risks across this company's sites." — the chat identifies the company's locations and screens each for heat, wildfire, flooding, and other hazards. * **Working with your own results.** From your assessment's home page: "Summarise my results", "What's missing in my assessment?", or "Explain the matrix to my CFO." ## Prompting tips * **Name the entity precisely**, or open the chat from that entity's page so it inherits the context automatically. * **Pick one lens and ask one thing at a time.** Use follow-up questions to drill in rather than packing several asks into one message. * **Ask "show your sources"** (or click the citation chips) when you want to see exactly what an answer is based on. * **Paste a short list** when you want to compare or screen several companies side by side. * **Turn on Web search** when you want external context (news, controversies); **attach a file** when you want the chat to reason over your own document. * **Tell it how strict to be.** By default the chat answers within Upright's framework, which may be looser than your own bar. If you want a tougher read, say so — for example, *"be strict: focus on additionality and direct impact, and flag anything weak or unproven."* * **Ask it to challenge you.** When you share your own view or document, you can ask the chat to push back rather than just agree — for example, *"what am I missing, and which of my assumptions would you question?"* ## What to expect — where the chat is strongest Treat this as "how to get the best results", not a disclaimer. The chat is most reliable on **entities Upright covers** and on Upright's own lenses: * **Strongest:** double materiality, net impact, financial effects, methodology questions, and peer comparisons or drill-downs for covered companies, funds, and products — anywhere the answer comes straight from Upright's structured data with citations. * **More variable:** companies with very thin public data, very large screens, hyper-specific single-number lookups, unusual or long product value chains, and site / climate questions where a company's locations are hard to find on the public web. When the chat is on weaker ground it will tell you what it could and could not establish rather than inventing a number — and naming the entity precisely, narrowing the lens, or splitting the question into follow-ups usually gets a better answer. ## What a response contains Responses are designed as analyst-style briefs — the chat does not narrate every number in the data; it summarises the headline story and surfaces the supporting numbers in structured elements next to the prose: * **KPI tiles** at the top — typically 1–4 headline numbers (for example, NET IMPACT, MATERIAL TOPICS, REVENUE) with a coloured tone indicator. The tone is based on the underlying valence of the metric, not on whether the news is good or bad for the user. * **Tables** below the prose for row-level data (dimensions, drivers, peer comparisons). Every table has a **Copy as TSV** and **Download as Excel** button in its title row. * **Inline citations** — small chips next to a claim that link back to the underlying tool result. * **Callout boxes** for verdicts, exec summaries, and "what this means" footers. * **Deep links** — the first time the chat mentions a company or a material topic, the name is linked to the corresponding platform page (DMA dashboard, single-matter detail, lens view, etc.) so you can jump straight from the chat to a deeper look. * **App cards** — for some questions (for example, a product value-chain query or a physical-climate-risk heatmap) the chat renders a dedicated visual card below the prose. The card is the diagram; the prose narrates it. ## Getting answers out, and languages * **Export** — every table has **Copy as TSV** and **Download as Excel** in its title row, and a full answer can be printed or saved to PDF from your browser's print dialog. The chat lays its answer out for you to read and copy from; it does not assemble a finished deck, Word document, or report file. If you need that, an external client connected via the [MCP connector](/upright-agent/external-clients-mcp) can drive its own document tools on top of Upright's data. * **Languages** — you can ask in your own language, and the chat will generally reply in kind. Upright's own vocabulary (impact-category and ESRS matter names) always stays in English. ## Follow-up questions Conversations are stateful. You can refer back to anything the chat just said: * "Now compare that to Microsoft." * "Drill into the negative environment score." * "Why does this score so negatively on biodiversity?" * "Show the supporting articles." If you want to start fresh, click **New chat** (in the chat header, or in the conversation-history sidebar on the standalone view). If the side panel was opened from a specific page, the page context follows you into the new conversation. ## Citations and sources Whenever the chat makes a claim that comes from Upright data, it attaches a citation chip linking back to the tool result. Click the chip to see exactly which data point supports the claim. When the chat lists or cites multiple companies, the answer ends with a **References** footer listing each one. Companies that are part of your organization's own tailored library (rather than the public Upright library) are marked with a small lilac **Private** chip after the name. ## Private vs. public results Some organizations have access to tailored variants of company results — for example, a CSRD assessment that uses your organization's own product list or sustainability data points instead of the public defaults. The chat answers within your own access rights: where your organization has a tailored variant of a company, the chat uses it by default; otherwise it uses the public Upright library. When the chat answers from a tailored variant, the company name carries a **Private** chip the first time it is mentioned. Public Upright library results are unmarked (no chip needed). If you are ever unsure which you are looking at, the chip is the tell — and you can simply ask "is this our tailored data or the public model?" ## Conversation history The chat history sidebar lists your previous conversations. Conversations are stored against your user account and are not shared with other users in your organization. ## What to do if the answer looks wrong The chat is grounded in Upright's structured data, but it is still an AI. If a response looks wrong: * Check the citation chips — they link to the underlying tool result and let you verify the source. * Ask the chat to "show your sources" or "what is this based on?" — it will surface the tool calls it used. * If you still believe the data itself is wrong, raise it with your Upright contact person; the feedback handling policy in the [appendix](/appendix/feedback-handling-policy) applies. # Data and tools available The data the chat can reach and the tools it uses to reach it. The chat does not produce numbers from a generic large language model. Every quantitative answer it gives comes from a structured tool call against Upright's own data — the same data that powers the rest of the platform. This page lists what the chat can reach and how it routes between sources. ## Analytical lenses The platform exposes six analytical lenses for a company, each with its own dedicated chat tool. The chat never reconstructs one lens's answer from another lens's data. * [**Net impact**](/metrics/net-impact) — Upright's headline impact score, broken down by category and dimension. Triggered by phrases like "net impact", "impact ratio", or impact category names (GHG emissions, jobs, knowledge, …). * **CSRD Double Materiality Assessment** — material topics under ESRS, plus drivers and supporting evidence. Triggered by phrases like "DMA", "double materiality", or ESRS matter names ("climate change mitigation", "own workforce", "biodiversity", …). * **Financial Effects** — monetary translation of sustainability risks and opportunities. Triggered by "financial effects", "EUR risk size", or "profit impact". Available on specific releases only. Beyond reading the headline figures, the chat can also size the financial effect of a single risk or opportunity you describe: it first echoes back the inputs it will use for you to confirm, then returns the monetised effect in the company's reporting currency. * [**UN SDG alignment**](/metrics/un-sdg-alignment) — alignment of a company's revenue with the UN Sustainable Development Goals, down to the individual SDG-target level, with per-product reasoning for why a product supports or undermines a given target. * [**EU Taxonomy**](/metrics/eu-taxonomy) — taxonomy eligibility and alignment, including green CapEx where available. * [**SFDR Principal Adverse Impacts**](/metrics/sfdr-pai-indicators) — PAI indicators for sustainability-related disclosures. Each lens has a dedicated platform deep link — when the chat answers from a lens, the first mention of the company (or topic) links straight to that lens's view in the platform. ## Single company, multi-company, group, and portfolio The chat keeps companies, groups, and portfolios as separate first-class concepts and picks the right tool for the right scope. * **Single company** — net impact, DMA, financial effects, SDG, EU Taxonomy, SFDR PAI for one named company. * **Ad-hoc list** — when you paste a few names ("compare Apple, Microsoft, and Alphabet"), the chat returns per-company rows side-by-side. Ad-hoc lists do not produce a list-level aggregate; for a portfolio-level aggregate use a real group or portfolio that lives in the platform. * **Group / portfolio** — when the platform already has a stored group (for example, an ETF, an index, a customer fund), the chat uses the group-level tools that return the platform's weighted roll-up, the holdings list, and the top contributors driving each impact category. * **Screening** — for ranked or filtered lists ("the top 10 European utilities by net impact"), the chat uses Upright's screen tool rather than guessing constituents. ## Coverage — which entities the chat can answer about The chat can answer about any company, fund, or product in Upright's coverage — on the order of tens of thousands of companies and funds and over a hundred thousand products. When you ask about something that is not covered, the chat says so rather than guessing, and (where relevant) can identify a company on demand from the public web. For the authoritative scope and how coverage is built, see the [Coverage](/coverage/off-the-shelf-coverage) section. ## External context: uploaded files and the web Beyond Upright's own data, the chat can also reason over two external inputs you control from the composer (see [Using the chat](/upright-agent/using-the-chat#attaching-files-and-searching-the-web)): * **Uploaded files** — a document you attach (for example, your own materiality assessment) that the chat reads alongside Upright's results. * **Web search** — public-web context, cited like any other source. The **Web search** toggle is on by default, but the chat leans on Upright's vetted data and only reaches the web when your question asks it to (see [Using the chat](/upright-agent/using-the-chat#attaching-files-and-searching-the-web)). ## Supporting evidence and drill-downs Beyond headline numbers, the chat can answer "why" questions by pulling in: * **Product-level reasoning** — why a specific product is assessed positively or negatively on a given impact. * **Supporting articles** — the scientific evidence Upright uses to ground its product impacts. * **Contributing products** — the products driving a company's score on a given impact category. * **DMA reasoning, overrides, and key takeaways** — the platform's own explanations for why a topic is or is not material. * **Single-matter summary** — drivers, IROs (impacts, risks, or opportunities), severity, and time horizon for one ESRS matter. ## Product value chain The chat can render Upright's product taxonomy as an upstream / downstream value-chain diagram (for example, "what feeds into steel" or "what does steel feed into"). The diagram appears as a card below the chat answer; the prose narrates the tiers and quotes the share-of-value-add percentages. ## Site identification and physical climate risk For questions like "where are X's offices / data centres / plants" the chat uses a public-web pipeline to identify site locations on demand. Combined with the climate-risk tools, it can also answer "is X exposed to wildfires / heat / heavy precipitation at its sites", for individual locations or aggregated across all of a company's sites. For broader spatial questions ("show climate risk across this region") the chat can render a grid-level climate-risk heatmap as a card below the answer, selectable by hazard, scenario, and time horizon. ## Knowledge base and methodology The chat has direct access to this knowledge base. Methodology questions ("how does Upright weight impact categories", "what does the SFDR PAI alignment column mean") are answered from the knowledge base content via the chat's search tool, with citations back to the relevant page. ## Comparisons across releases The customer-facing chat is scoped to Upright's **latest** model release. Comparisons against older releases ("how did the 4.x DMA differ from now") are not supported on the chat surface — they require direct platform access. ## What the chat never does * **Never fabricates numbers** for a lens that has no data for a given company. If SDG / EU Taxonomy / SFDR PAI data is not available for a company, the chat says so rather than estimating from net impact or DMA figures. * **Never reconstructs one lens from another** — for example, it does not produce SDG alignment by re-deriving it from net impact, or estimate DMA materiality from SDG alignment. * **Never invents methodology coefficients** — for example, the financial-effects formula is multiplication of three named factors with no hidden weighting coefficients, and the chat will not invent any. # Connecting external AI clients Reach the same tools from Claude Desktop, Claude.ai, Microsoft Copilot Studio, and other MCP-compatible AI clients. The chat tools described in [Data and tools available](/upright-agent/data-and-tools) are also exposed as a [Model Context Protocol](https://modelcontextprotocol.io) (MCP) server. This lets you reach the same Upright data from external AI clients — Claude Desktop, [Claude.ai](https://claude.ai), Microsoft Copilot Studio, and other MCP-compatible clients — instead of (or in addition to) the in-platform chat. The MCP endpoint and the in-platform chat share one tool set and one set of guardrails; the answers are the same regardless of which client you ask from. ## Endpoint The MCP server is available at: ``` https://api.uprightproject.com/api/mcp ``` It speaks the standard MCP HTTP transport with OAuth 2.0 authentication (Dynamic Client Registration). Any spec-compliant MCP client can connect to it without Upright-specific wrapping. ## Access {% hint style="warning" %} The MCP connector is controlled by a **separate permission** from the in-platform chat. Depending on your account type it may already be enabled alongside the in-platform chat, or it may need to be turned on for your organization. If you cannot connect, contact your Upright contact person or write to
| Impact category | Description | Example |
|---|---|---|
| Jobs | Employing people and thus enabling them to gain financial actorship and identity in society. In this impact, we take into account people employed both directly by the company, as well as indirectly by its suppliers and customers. | A company employs 700 people. |
| Taxes | Contributing to joint resources via taxes paid directly by the company or indirectly by its suppliers and customers. | A company pays its corporate taxes. |
| Societal infrastructure | Contributing to or forming basic societal infrastructure, such as roads, sewage systems, electricity networks, hospitals, schools, and pension systems. This can occur either directly through the company’s core products and services, indirectly through its suppliers' operations or when its products and services are used by customers. | A company builds sewer systems. |
| Societal stability & understanding among people | Increasing understanding among people or inciting or enabling peace. This can occur either directly through the company’s core products and services, indirectly through its suppliers' operations or when its products and services are used by customers. | A company offers translation services that help people with no common language understand each other. |
| Equality & human rights | Increasing racial, economic or gender equality, or enforcing human rights. This can occur either directly through the company’s core products and services, indirectly through its suppliers' operations or when its products and services are used by customers. | A company provides microloans to women in developing countries, enabling them to start a business. |
Figure 1: Illustrative example economy
Figure 2: Summary of Scope 1/2/3 emission metrics in the example economy
Figure 3: Attribution of GHG tons to the companies in the example economy with the algorithm used by Upright
# Differences of net impact results and company disclosures This page describes how net impact data differs from GHG inventories or other bottom-up company disclosures. Upright's net impact model [accounts for direct and indirect impacts](broken://pages/6Z2MgQK3ukbHrMG8o5wM#both-direct-and-indirect-impacts-are-taken-into-account) but [avoids double counting](broken://pages/6Z2MgQK3ukbHrMG8o5wM#impact-is-attributed-once-and-only-once). Therefore quantification of impacts like emissions, tax payments and job creation may considerably deviate from corresponding values produced with different methodologies. ## GHG inventories Scope 1, 2 and 3 emissions as defined by the Greenhouse Gas Protocol include double counting of emissions of related companies: > By definition, scope 3 emissions occur from sources owned or controlled by other entities in the value chain (e.g., materials suppliers, third-party logistics providers, waste management suppliers, travel suppliers, lessees and lessors, franchisees, retailers, employees, and customers). Scope 3 emissions for the reporting company are by definition the direct emissions of another entity. \[...] Because of this type of double counting, scope 3 emissions should not be aggregated across companies to determine total emissions in a given region. \[...] companies should acknowledge any potential double counting of reductions or credits when making claims about scope 3 reductions > > \[...] double counting is a problem when it comes to offset credits or other market instruments that convey unique claims to GHG reductions or removals. If GHG reductions or removals take on a monetary value or receive credit in a GHG reduction program, it is necessary to avoid double counting of credits from such reductions or removals. > > \- [GHG Protocol Scope 3 FAQ](https://ghgprotocol.org/sites/default/files/standards_supporting/Scope%203%20Detailed%20FAQ.pdf) The Upright net impact model attributes total emissions across the whole private sector, meaning that impact is not double counted. This makes emission values in the net impact model incomparable with companies' bottom-up GHG inventories. (Read more in [Essential principles](broken://pages/6Z2MgQK3ukbHrMG8o5wM).) ## Direct tax payments and job creation Tax payments and employment numbers reported by companies relate to direct impacts. They do not consider indirect impacts enabled by the company. On the other hand, such direct disclosures do not account for indirect influence that other companies in the value chain might have on the direct impacts of the company. When considering a the tax payments or job creation of a company, the Upright net impact model credits the company for some indirect impact it has in its value chain, but it also credits companies in the value chain for direct impacts of the company. This avoids double counting impacts across the private sector, but consequently the figures are not expected to match companies' disclosures of direct impact. (Read more in [Essential principles](broken://pages/6Z2MgQK3ukbHrMG8o5wM).) # Indicative guidelines for classifying investments in line with SFDR This document provides information on using Upright data to classify investments in line with the EU Sustainable Finance Disclosure Regulation (SFDR). {% hint style="warning" %} **No legal advice** Upright is an impact data provider. *The contents of this document do not constitute legal advice, are not intended to be a substitute for legal advice, and should not be relied upon as such. You should seek legal advice or other professional advice in relation to any particular matters you or your organisation may have.* {% endhint %} ## Background ### The need for classifying investments The EU Sustainable Finance Disclosure Regulation requires classifying funds into three categories: * Article 6 ("grey") * Article 8 ("light green") * Article 9 ("dark green") Conceptually, the difference in these three classes of funds is related to the *sustainability promise* they provide. Article 9 funds have sustainable investment as objective, while Article 8 funds merely have *sustainable or environmental characteristics*. Article 6 funds don't make any sustainability-related promise, while still possibly taking sustainability risks into account as part of normal risk management. In addition to the fund's classification, Article 8 and Article 9 funds must disclose how (they plan to) allocate assets in terms of the sustainability of their investments. According to the regulation, the asset allocation must be stated using the categories summarized in the following table:
| EU category | Upright dimension | Upright impact category | Threshold |
|---|---|---|---|
| Social | Society | Societal infrastructure | 6.8 |
| Social | Society | Societal stability | 5.4 |
| Social | Society | Equality & human rights | 2.5 |
| Social | Health | Physical diseases | 7 |
| Social | Health | Mental diseases | 3.3 |
| Social | Health | Nutrition | 15 |
| Social | Health | Relationships | 9.4 |
| Social | Knowledge | Knowledge infrastructure | 3.8 |
| Social | Knowledge | Creating knowledge | 4.4 |
| Social | Knowledge | Distributing knowledge | 10.8 |
| Social | Social | Group of select social impacts* | 13.5 |
| Environment | Environment | Group of all environmental impacts** | 3.6 |
| Upright dimension | Upright impact category | Examples of company activities exceeding the threshold |
|---|---|---|
| Society | Societal infrastructure |
|
| Society | Societal stability |
|
| Society | Equality & human rights |
|
| Health | Physical diseases |
|
| Health | Mental diseases |
|
| Health | Nutrition |
|
| Health | Relationships |
|
| Knowledge | Knowledge infrastructure |
|
| Knowledge | Creating knowledge |
|
| Knowledge | Distributing knowledge |
|
| Social | Group of select social impacts* |
|
| Environment | Group of all environmental impacts** |
|
| EU category | Upright dimension | Upright impact category | Threshold |
|---|---|---|---|
| Social | Society | Societal stability | 14 |
| Social | Society | Equality & human rights | 1.3 |
| Social | Health | Physical diseases | 10 |
| Social | Health | Mental diseases | 12 |
| Social | Health | Meaning & Joy | 6 |
| Social | Health | Relationships | 5 |
| Social | Knowledge | Distributing knowledge | 2 |
| Environment | Environment | GHG emissions | 12 |
| Environment | Environment | non-GHG emissions | 5 |
| Environment | Environment | Scarce natural resources | 4 |
| Environment | Environment | Biodiversity | 7 |
| Environment | Environment | Waste | 3.5 |
Summary of criteria Upright uses for determining NFRD status. The national thresholds have been sourced from the European Financial Reporting Advisory Group's (EFRAG)'s assesment report.
# Communicating Upright's data – Corporates This article provides examples of how companies using Upright's data discuss it in their reporting and regulatory disclosures.
| Organization | Industry | Country | Datasets | Report |
|---|---|---|---|---|
| Barona | Personnel services | Finland | Net impact | Responsibility Report 2023 |
| Enersense | Construction | Finland | Net impact, SDGs | Annual Report 2023 |
| Fiskars Group | Consumer goods | Finland | Net impact | Sustainability Report 2023 |
| Framery | Office furniture | Finland | Net impact, DMA | Sustainability Report 2023 |
| GRK | Construction | Finland | Net impact, DMA | |
| Lamor | Environmental services | Finland | Net impact, DMA | |
| Lindström | Textiles | Finland | Net impact | Net impact article |
| Medicover | Healthcare | Sweden | Net impact, SDGs | Annual and Sustainability Report 2023 |
| Orion | Pharmaceuticals | Finland | Net impact | Sustainability Report 2023 |
| Revenio | Healthcare | Finland | Net impact | Sustainability Report 2023 |
| Securitas | Security services | Sweden | Net impact | Annual and Sustainability Report 2023 |
| Investor | Type | Country | Datasets | Report |
|---|---|---|---|---|
| AC Ventures | Venture Capital | Indonesia | Net impact | Impact Report 2022 |
| Aktia | Asset Manager | Finland | Net impact, SDGs, DMA | Annual Review 2023 |
| Aktia | Asset Manager | Finland | Net impact, SDGs | Quarterly Impact Report Q2/2024 |
| Aktia | Asset Manager | Finland | Net impact | Overview of responsible investment H1/2024 |
| Altor | Private Equity | Sweden | PAI | Sustainability Report 2023 |
| Brightlands Venture Partners | Venture Capital | The Netherlands | Net impact, SDGs | Impact Report 2023 |
| Brightlands Venture Partners | Venture Capital | The Netherlands | Net impact | Responsible Investment Policy |
| Butterfly Ventures | Venture Capital | Finland | Net impact, SDGs, EU taxonomy, PAI | SFDR Periodic Reporting Q2/2024 |
| Churchill Asset Management | Private Equity | United States | Net impact, SDGs, PAI, EU taxonomy | Sustainability Report 2023 |
| Juuri Partners | Private Equity | Finland | Net impact, SDGs, PAI | Annual ESG Report 2023 |
| LGT Capital Partners | Private Equity | Switzerland | Net impact | ESG Report 2023 |
| LGT Capital Partners | Private Equity | Switzerland | Net impact, PAI | SFDR Website disclosure |
| Mandatum | Asset Manager | Finland | Net impact, PAI | Responsible Investment Review 2023 |
| Nordea | Asset Manager | Finland | SDGs | Fund Portrait Nordea 1 |
| Norselab | Venture Capital | Norway | Net impact, SDGs, EU taxonomy, PAI | Meaningfulness Report 2023 |
| RBC BlueBay | Asset Manager | United Kingdom | Net impact, SDGs | Sustainability Impact Report 2024 |
| Taaleri | Asset Manager | Finland | Net impact, PAI | Annual Report 2023 |
| Voima Ventures | Venture Capital | Finland | Net impact | Responsible Investment Policy |