Investment funds are responsible for substantial environmental and social impacts with trade-offs

Investment funds are responsible for substantial environmental and social impacts with trade-offs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Investment funds are responsible for substantial environmental and social impacts with trade-offs Ioana Popescu, Thomas Schaubroeck, Thomas Gibon, Claudio Petucco, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3345219/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Jun, 2024 Read the published version in Communications Earth & Environment → Version 1 posted You are reading this latest preprint version Abstract As finance takes on a pivotal role in sustainability transitions, regulations that mandate impact reporting at financial product level are emerging worldwide. Without standardized and reliable indicators, sustainable investing risks being watered down by greenwashing. We show that life-cycle-based, ready-to-use impact indicators can be adapted for sustainability assessment of financial products. We designate 13 environmental and 13 social impact indicators aligned primarily with the EU Sustainable Finance Disclosure Regulation, one of the first pieces of regulation mandating sustainability reporting for financial products. Two major advancements of our framework are the coverage of social impacts and the direct policy relevance. As practical application, we estimate the impacts of a sample of 230 self-labelled sustainable investment funds for all indicators. We show that these investment funds are exposed to significant environmental and social impacts via their investee companies. Total estimated impacts vary between 2.1 and 28.4 times the impacts associated with the consumption of a one million EU citizens, depending on the indicator. Trade-offs could be signalled between and within environmental and social indicators. Finally, most impacts can be traced to a small number of publicly listed companies, which investors could engage with, in order to drive change. Earth and environmental sciences/Environmental sciences/Environmental impact Earth and environmental sciences/Ecology/Environmental economics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Monetary flows towards so-called sustainable investment funds are projected to grow to one third of the global market by 2025 (53 trillion USD) 1 . However, the effectiveness of sustainable finance in driving changes in the real economy is heavily scrutinized 2,3 as there is no top-down, thorough assessment of their sustainability claims 4,5 , leading to an increased risk of greenwashing 6 . Nonetheless, this risk is being progressively compensated from a bottom-up perspective by the growing focus on measuring the sustainability of financial products such as investment funds. Investors’ awareness is, among others, driven by the imminence of climate (but also other environmental and social) risks that can jeopardize the long-term profitability of investments 7 . Indeed, previous studies 7,8 highlighted the significant hidden exposure to losses in the financial markets due to climate change. Next to policy makers, investors are an additional lever to push for a sustainable economy, by exerting influence on the companies they invest in 9 . Having access to reliable and complete sustainability information is vital to the integration of sustainability in the investment decision making process 10,11 . Existing environmental, social and governance (ESG) ratings, widely used as proxy for sustainable performance, are mostly unreliable and do not offer sound quantitative information to investors 12,13 . To tackle this issue, initiatives to standardize sustainability assessment at financial product level are emerging globally 14 , with the regulations under the EU Sustainable Finance Action Plan being regarded as the most ambitious 15,16 . The Sustainable Finance Disclosure Regulation (SFDR) 17 is the piece of legislation addressed to financial institutions and is the driver of our research. However, indicators under the SFDR are not comprehensive enough, while at the same time reliable company data to aid in sustainability reporting is unavailable. In this paper, we demonstrate that science-based, ready-to-use, environmental and social indicators aligned with regulations can already be operationalized in a coherent framework for sustainability assessment of investment funds. To define the framework, we begin by analysing sustainable finance reporting regulations, identifying links with indicators from the best-practice sustainability assessment framework of life cycle assessment (LCA). As fostered to a considerable degree in regulations 18,19 , LCA can provide consistent estimation of sustainability impact at product and organisation level 13,20,21 . Adopting a life cycle perspective to impact assessment is crucial in order to avoid shifting of impact to indirect stages of production and consumption. Moreover, environmental LCA methodology includes a comprehensive set of impact indicators, to avoid trade-offs between these 13,22 . Importantly, social impact indicators can be retrieved using Social LCA 23 , which has not yet been broadly applied in this context. As a first contribution, we designate 13 environmental and 13 social life-cycle-based impact indicators that can be estimated at company and investment fund-level using input-output LCA (IOLCA) 25 (Methods, Table 1 and Table 2 ). In our innovative approach, we are linking two different input-output databases with financial information at company and investment fund level, and we extend the approach of Popescu et al. 24 , which focused on carbon footprint of investment funds, to others impact indicators, both environmental and social. As a second contribution, we apply this framework to a sample of 230 investment funds. The sample represents all self-labelled sustainable equity funds listed on the Luxembourg Green Exchange, for which complete information could be retrieved. This is the biggest exchange for green financial products, which represent more than 13% of the assets under management (AuM) of all funds self-labelled sustainable under the EU SFDR 26 (so-called article 8 and article 9 funds). To calculate impact at investment fund level, we first apply IOLCA analysis to estimate impact factors at country-sector level 27,28 , and then at company level, using the country and sector distribution of a company’s revenue. This data is then aggregated at investment fund level. This approach has been applied by ourselves and others in past work 24,29 . In the analysis of these results, we first study the spread between funds for each impact category, and the correlations between impact categories and within the group of social and the group of environmental indicators, to identify synergies, and trade-offs. Subsequently, we analyse the magnitude of impact per impact category, attributed to the funds’ sample and we discuss the differences between direct and indirect share of impact. To better grasp the amplitude of impact attributable to funds, we compare it to the impact attributable to the consumption of all EU citizens over one year. As a final analysis, we look at the top funds holdings that drive the lion’s share of fund-level impact and discuss implications. Results A set of consistent and ready-to-use environmental and social impact indicators The three main pieces of regulations that mandate sustainability-level disclosures are: the EU Sustainable Finance Disclosure Regulation 17 (SFDR), the EU Corporate Sustainability Reporting Directive (CSRD) 30 , and the EU Taxonomy 31 . In our framework development for indicators (Fig. 1), we first define the link between environmental and social objectives from the EU Taxonomy and then indicators proposed under SFDR (the so-called Principal Adverse Impact (PAI) indicators for investments in investee companies) and CSRD. We draw the (mis-)matchings between the sustainability indicators proposed. In a last step to define our framework, we link life-cycle-based indicators (last column in Fig. 1), that are ready-to-use and science-based, which could be thus used in reporting against the SFDR regulation (as further discussed in Methods and Supplementary Information - SI). The comprehensiveness and rigorousness of SFDR-proposed indicators is unsatisfactory, when compared to the state-of-the-art indicators in the sustainability assessment field. First, there is an inconsistent coverage of sustainability issues, as compared to widely accepted frameworks for sustainability assessment. Second, disclosure over the life cycle is not mandated. Finally, there is no clear methodology to underpin the indicators proposed, which may lead to reported data not being comparable between financial institutions (argumentation is further detailed in the SI). The EU environmental footprint (EF) method 32 and the UNEP Social LCA Guidelines 23 are chosen as guidance frameworks. These frameworks are among the most consensual at policy level and are relying on science-based sustainability assessment methods. The selection of ready-to-use, life-cycle-based indicators is restricted, in a first step, by the availability of raw environmental and social indicators in the IOLCA databases of choice. For environmental impacts, we use indicators that can be estimated using the environmentally extended multi-regional input-output database EXIOBASE, which was employed for similar purposes 29,33 and has a detailed country and sector-level coverage. For the social impact assessment, the PSILCA database 34 is used, which has a large coverage of social indicators and detailed country and sector-level coverage for EU countries (Methods). Life-cycle-based ready-to-use indicators have been found for almost all regulation objectives/indicators. Concerning well-matching environmental impact topics, climate change mitigation is the only objective with a one-to-one relationship between all standards, validating the maturity level and consensus on this main environmental issue. For SFDR PAI indicator “emissions of air pollutants” (like ammonia – NH 3 ), the EF method provides several impact indicators to assess their effects – namely terrestrial acidification and eutrophication 32 . Similarly, “emissions to water” and “emissions of inorganic pollutants” are represented by the corresponding life cycle impact indicators of toxicity and eutrophication . The SFDR PAIs of “land degradation” and “activities negatively affecting biodiversity-sensitive areas“ can be grouped under the life cycle impact indicator of land-use related biodiversity loss 35 . For “water use” and “exposure to areas of high water stress”, we propose the alternative indicator of water stress , which weights water usage based on the characteristics of the region where it takes place – whether the area is more at risk of water stress or not 35 . An exception is the EU taxonomy objective of climate change adaptation having no standardized equivalent indicator in LCA. In the extended literature, there are however many examples of indicators specifically developed to measure adaptation, e.g., flood safety levels for a stormwater management system 36 measuring the impact compared to a reference scenario. In some cases (e.g., electricity generation) the same indicators used for climate change mitigation can apply 18 . Similarly, for the “circular economy” (CE) objective, indicators measuring circularity do not directly represent environmental impacts, instead they are a proxy (or not) for better (or worse) environmental performances which shall anyway be measured separately. Multi-dimensional scoring tools that serve as CE indicators have been previously developed and tested, such as the Circularity Potential Indicator 37,38 . Life-cycle-based indicators assessing resource use and scarcity 39 may be used to this aim, until better indicators based on reliable collected data will be developed 38,40 . Social indicators from regulations tend to be qualitative (Fig. 1 Panel B). For example, the scope of the SFDR-proposed indicator of “violations of UN Global Compact principles and OECD Guidelines for Multinational Enterprises” is too broad and would not give stakeholders a sense of the social impacts that underline a funds’ portfolio. While social impacts are by default driven by more abstract characteristics of a company – such as employee policy – more quantitative indicators can be developed, that allow for a clearer assessment of a company or investment. Hereto, quantitative indicators by social impact category are being developed in social LCA 23,41 . We propose semi-quantitative risk-based indicators available in the social IOLCA database PSILCA, that can be estimated at sector, company, and financial product level. Examples of PSILCA indicators are rate of accidents, children in employment, right to collective bargaining. Application to a representative sample of sustainable funds We applied the indicators framework to a representative sample of 230 equity funds, self-labelled sustainable under the SFDR classification, investing collectively in over 4,800 unique public companies worldwide. The sample represents the unique universe of SFDR Article 8 and Article 9 funds listed on the Luxembourg Green Exchange. The funds in the selected sample hold together 401 billion USD (US dollar) of investments, which, if compared to the size of an economy, is approximately as large as the gross domestic product (GDP) of Denmark over a year (398.3 USD billion in 2021, according to the World Bank data 42 ). Widely spread impact distribution for the funds sample For the environmental impact assessment, the results at fund level vary by indicator (Fig. 2), with a widely spread distribution of impacts among Article 8 and Article 9 funds. Acidification, eutrophication and ecotoxicity impact intensities have highest spreads (for example, direct freshwater ecotoxicity impact indicator mean across funds is of 19.2 thousand CTUe/MUSD, with a standard deviation of 43.2 thousand CTUe/MUSD), while for the climate change indicator we see a smaller interval for the results distribution (245 tCO 2 /MUSD mean, and standard deviation of 481 tCO 2 /MUSD). All values are displayed in SI Table S1. For the set of social impact indicators, the impact range is more concentrated, especially for the Article 9 funds. Yet, outliers appear across all impacts. These can be driven by investments in companies which have activities in countries with large relative impacts, or be skewed towards specific industries, thus leading to a much larger result than the sample mean. Finally, these results show that choice between different funds may be associated with big differences in sustainability impact. Synergies & trade-offs between social and environmental impacts Environmental and social impacts at fund level are not always strongly and positively correlated, as shown in the matrix of Spearman’s rank correlation coefficients in Fig. 3, implying that an investment fund or a company can rank highest for one indicator, while scoring lower for other indicators. In general, we observe high correlations within each distinct subset of social and environmental indicators, and low correlations in between the two, meaning that there are larger trade-offs in between environmental and social indicators than within the environmental or social indicators’ groups. Within the set of environmental indicators, some strong synergies are visible, whenever impact indicators are derived from common environmental flows. For example, acidification is highly correlated (coefficient larger than 0.9) with terrestrial eutrophication and photochemical ozone formation: expectedly so as ammonia emissions are contributing to all these three impact indicators. Human toxicity is correlated to ecotoxicity as heavy metals emissions are an important contributor to these impact indicators. At the opposite, for human toxicity we observe trade-off with water stress, given the low degree of similarity between the two impact indicators. Another trade-off observed at fund-level is for indicator climate change with land-use related biodiversity loss. Biodiversity loss would thus be a very important indicator to measure alongside climate change, in order to avoid causing more harm for biodiversity when investing with reduction of GHG emissions as main goal. Within the set of social indicators, there is a predominance of very high correlations. At fund level, both the portfolio allocation and the sector-country distribution of the held companies’ revenue drive the correlation coefficients. Given the fact that funds tend to have high similitude in portfolio allocation – if, for example, more funds follow the market index – correlation is higher at fund level. If we do the exercise at company-level, the inherent economic activities of the company drive the correlations. In the Supplementary Information Figure S3, we show correlation matrixes between all impact indicators at company level, separately by main sector group. For Retail and Wholesale Trade sectors, there are strong negative correlation coefficients between most of social and environmental categories, signalling a high trade-off when investing in these industries. Indeed, the trade sector can be described as having low environmental pressures, while having a high impact on workforce. On the contrary, for Transportation and Utilities companies, we see a weaker negative relation between social and environmental indicators, while we observe more pronounced negative correlations between environmental indicators – especially between particulate matter and the other indicators, as companies with very high particulate matter impacts rank lower on other environmental indicators. Companies in the Mining industry are perhaps the most interesting, as they show strong trade-offs between social and environmental indicators, but also within environmental indicators (namely material footprint vs. the rest of environmental indicators). Magnitude of direct and indirect impacts at fund level We estimate the total life cycle impact of the funds sample, based on the amounts invested, and plot the proportion of direct and indirect impacts (covering the upstream life cycle chain) alongside absolute direct and indirect impacts (Fig. 4). For all indicators, indirect impacts are considerably larger than direct impacts. However, results vary by impact indicator. Indirect proportion is lower for particulate matter, photochemical ozone formation and GHG emissions, where the contribution of direct impacts to the total life cycle impact is higher than 40%. Similar contribution is observed among the social indicators, namely for lack of rights of association and rights to strike. To contextualize the estimated impacts of the funds sample, we express this in the population equivalents of the impact of EU citizens (assuming a EU population of 447 million citizens in 2019), based on the categories of the EU final demand in EXIOBASE (household and government consumption, inter alia). The funds’ investments are equivalent to the impacts of between 2.1 to 28.2 million EU citizens, depending on the impact indicator chosen. For example, for climate change mitigation, the total sample of investment funds is responsible for 62.2 million tons of CO 2 -equivalents (MtCO 2 -eq) direct emissions and 70.9 MtCO 2 -eq indirect emissions. This is equivalent to the life cycle climate change impact attributable to the final consumption of Belgium in 2019 (11.5 million inhabitants), corresponding to 146.4 MtCO 2 -eq, based on input-output calculations. The variation in the million EU citizen equivalents is explained by the different drivers of impact for final demand versus funds’ holdings. First, the investment pool of a fund investing in global public companies tends to be skewed towards companies from Finance, Services and Tech industries, as these are the companies with largest market valuation and largest share in the capital markets 24 . Companies in these industries generally have low direct environmental burdens (for finance companies, second-order impacts, via their investments, are not conventionally counted via the life-cycle-based method), compared to consumption goods, which may play a larger role in the final demand attributable to EU citizens, hence the lower citizen amount equivalent in terms of environmental impacts. Second, for some environmental and social categories, we expect European consumption to be more intensive than an average sample of global public companies (as one could describe the funds’ holdings). For example, for the social indicators of trafficking in persons, restricted right to strike and restricted rights of association, the sample of funds has 20 times higher impacts than the total footprint of all EU citizens. Concentration of impact in key industries and large companies We identify companies (grouped by main industry) that drive the lion’s share of impact, by impact category. These companies drive in aggregate more than 50% of the total life cycle impacts estimated for the funds sample. In Fig. 5, we show results only for a handful of impact indicators, with all the other results in the SI (including listing of top companies in Supplementary Dataset 1). Fund-level allocation seems concentrated around a very small number of large corporations, meaning that the studied funds tend to hold similar large companies in their portfolios, with different holding amounts. The list of companies at the top varies depending on the impact category analyzed. For water stress indicator, 50% of the life cycle impact can be traced back to only 27 large corporations (out of almost 5,000 different companies). Funds hold aggregated positions amounting to 28 billion USD in these 27 companies (representing only 8.3% of all positions held). The main positions driving impact being investments in Nestlé, Unilever, and Danone – all three large companies from the fast-moving consumer goods (FMCG) sector. It is expected to see FMCG companies to bear the largest share, as these depend on manufacturing of diverse products, but also cultivation and processing of raw materials, in the case of the food processing sub-sector. Similar importance of FMCG companies is seen for land-use related biodiversity loss indicator, while companies in the Paper sector also play a large role here (due to deforestation impacts). For human toxicity impact indicator, companies in the Information Technology sector (IT) have, in aggregate, the highest contribution (largest companies being Schneider Electric SE, Samsung Electronics, and Siemens AG). IT companies, including semiconductor manufacturers, have large market values, hence the large exposure of funds. The high values for human toxicity are driven by the need of metals and other chemical compounds in the manufacturing phase. For climate change impacts, companies from the Utilities and Oil & Gas sectors drive the largest share of impact (biggest contributors Enel SpA, China Petroleum, and Iberdrola SA). For social impacts, there is a more even distribution of impacts between industries invested in. Surprisingly different to the environmental impacts is the prevalence of Finance and Services sector companies as high contributors to negative social impacts. This is because social issues tend to be more prevalent in finance and services-related sectors. Moreover, for indicators anti-competitive behavior and children in employment, companies from the Industrials sector also have a very high contribution. Discussion The literature on sustainability assessment of investment products is scarce, focused on GHG emissions 24,29,43 , despite the call for better alignment of capital markets with sustainability goals beyond reduction of carbon emissions 44 . Previous literature has assessed the climate performance of investment funds 45 and the exposure to climate risk 7,8 , but has not analysed in parallel multiple environmental and social impact indicators. In addition to previous literature studying connections between EU Taxonomy and LCA 19 , we link specific indicators from EU SFDR requirements with ready-to-use life-cycle-based indicators. It is clear from our results that trade-offs between and within social and environmental impact categories occur. Focusing on one or a few impact indicators in the detriment of others, could lead to doing more harm than good. As stipulated by the EU Taxonomy, impact assessment should include both environmental and social considerations, in order to avoid impact shifting within environmental categories (for example, green electricity can lead to reduction in GHG emissions but may cause negative impact on biodiversity) or missing impacts on categories of stakeholders not considered when looking at social issues (for example violating the rights of indigenous people by approving construction/deforestation on native lands). When looking at company-level correlations in terms of impacts, we see more specific trade-offs. For example, investments in Utilities companies have better scores on social issues and worse score on environmental issues. Our results strengthen previous findings that climate change cannot be used as a proxy for all environmental impacts 46 contributing to the debate on indicator proxies 47,48 . Compiling a full set of ready-to-use indicators is delimited by the availability of input-output databases with associate impact assessment methods of adequate quality. IO databases, like EXIOBASE and PSILCA, and LCA methodology in general are under continuous improvement and future developments will likely lead to more accurate and complete estimates resulting from our proposed framework. Our modelled results are susceptible to uncertainty coming, among else, from the limited level of detail in revenue reporting – the coarser the level of reporting, the higher the risk to have under- or overestimated impacts. For example, this is the case for the company Iberdrola SA, where information on the type of electricity produced is not available in FactSet, leading to overestimation of impact for climate change indicator, as average electricity generation impact factors are then alternatively considered. Therefore, better reporting from the company side at the level of economic activities undertaken is necessary. In parallel, sustainability reporting requirements at investment level should develop to include more measurements of additionality and contribution, in addition to intensity metrics, in order to account for the transition plans of companies. Our results provide evidence that impacts attributable to funds are substantial. The large share of indirect impacts, previously highlighted at industry level 27 , is also to be observed at fund level. Moreover, if we regard investment funds as entities carrying the responsibility for their investment, their environmental and social footprints are comparable to that of EU consumers, albeit much larger given the high value of capital markets. Hence, we call upon a stricter approach to disclosure requirements in terms of indirect impacts. For example, measuring and setting indirect impact targets allows investment managers to exert influence over the companies in the supply chain, thus increasing the potential engagement opportunities 9 . In addition, we have observed a strong concentration of large publicly listed companies in the portfolios of analyzed funds. Depending on the impact category, the industry and companies contributing most impact are shifting. The large exposure to certain companies and impact hotspots can be a driver of engagement with companies, demanding improvements in environmental and social practices 9 . Our analysis can serve as a baseline for harmonizing sustainable finance regulations and science-based sustainability assessment and its impact indicators. Standardization would facilitate comparability and reliability of indicators. Main strength of our approach is that impacts are estimated using the same background methodology data for all environmental indicators; for social indicators, similar methodology but a different underlying input-output database are used. Irrespective of the type of reporting requirements or their location, our proposed set of indicators is embedded in international practice related to sustainability assessment and can thus serve as a general framework for sustainability assessment at financial product level. While outside the scope of the current paper, we acknowledge the importance of assessing the state of governance, at investment fund and company level. A robust governance policy at entity level would ensure the implementation of policies and activities that are helping advance the environmental and social agenda. A long-term perspective in value creation for stakeholders 49 is in harmony with pursuing environmental and social objectives that usually have a much larger time frame to materialize than financial objectives 50 . Methods Selection of life-cycle-based indicators As starting point for proposal of the 26 environmental and social indicators, we had the so-called Principal Adverse Impacts (PAIs) proposed under the Sustainable Finance Disclosure Regulation (SFDR), which are defined under the SFDR’s Regulatory Technical Standards (RTS). As shown in SI Figure S2, we have divided the SFDR PAI indicators between inventory (specific environmental flow) and impact indicators (translation of effect of environmental flows on a certain impact type). Furthermore, we looked at how the SFDR indicators match with the six environmental objectives proposed under the EU Taxonomy and its Minimum Social Safeguards. Afterwards, we analysed the Corporate Sustainability Reporting Directive (CSRD), which, via its European Sustainability Reporting Standards (ESRS), makes reference to science-based measurement methods and standards on greenhouse gases emissions 51 , such as ISO:14046-1:2018, and on other environmental indicators, as the Environmental Footprint methods 32 (EF). More detailed analysis of the legislative framework around EU sustainable finance reporting can be found in the SI and in the tables of Supplementary Dataset 2. Afterwards, we have considered current state-of-the-art in terms of life-cycle-based indicators. Especially for the environmental dimension, the difficulty in choosing a sufficient and comprehensive set of indicators, lies in the existence of multiple methods to assess the same impact category, even for established impact categories, like climate change. We preselected the Environmental Footprint (EF) version 3.1, updated in 2022 is proposing a set of 16 impact indicators and underlying methods and characterization factors, as ( 1 ) it is based on latest literature developments, ( 2 ) can be linked to ready-to-use indicators built using EXIOBASE 52,53 (the selected environmentally extended input-output database) and ( 3 ) it is brought forward by EU policy. From these 16, we only selected 13 environmental indicators (Table 1 ), with exclusion of two EF indicators that are not covered in the IOLCA data (ozone depletion and ionizing radiation), and the summing of two resource use indicators into represent the material footprint indicator. While the EF is considerably up to date in terms of impact indicator assessment methods, it does not integrate aspects like the difference in impact magnitude caused by the location where the impact takes place, which is highly relevant, especially for water stress 54 , pollution 55 , toxicity or biodiversity. Therefore, for water use, particulate matter, and land-use impact indicators in the EF methods, we use impact factors corresponding to location-specific impact indicators of water stress, particulate matter, and land-use related biodiversity loss, that are built using EXIOBASE. These are obtained from the work of Cabernard et al. 35 . The set of life-cycle-based indicators had to be further reduced because of missing coverage of environmental flows in the input-output database chosen. The missing coverage has been previously discussed in literature and input-output analyses would benefit in the future from the inclusion of other key environmental flows 52 , such as different toxic substances 52 , or pesticides 56 . Table 1 13 environmental impact indicators for sustainable finance reporting, based on Environmental Footprint version 3.1. and EXIOBASE. In the first column we list the EU Taxonomy environmental objectives. In the second column, we make the connection to SFDR PAIs. In the third column, we list the final set of 13 environmental indicators. In the last column we describe the environmental flows that are included to compute an impact indicator. An impact indicator is the sum of the impacts resulting from the environmental flows, calculated by multiplying each one of these with its corresponding characterization factor (i.e., the impact per unit of environmental flow estimated using different impact assessment models). EU Taxonomy Objective SFDR PAI Proposed EF impact category Unit (environmental impact category indicator) Source Included environmental flows climate change mitigation GHG emissions Climate change, total GHG emissions, GWP100 (kgCO 2 eq) 52,53 CO 2 , CH 4 , N 2 O, SF 6 , HFC, PFC climate change adaptation n/a n/a n/a n/a n/a the sustainable use and protection of water and marine resources emissions to water; inorganic pollutants Ecotoxicity, freshwater Comparative Toxic Unit for ecosystems (CTUe) 52,53 Benzo(a)pyrene, Indeno(1,2,3-cd)pyrene, PCDD_F, HCB, As, Cd, Cr, Hg, Cu, Ni, Pb, Benzo(k)fluoranthene, Se, Zn, B(a)P, Indeno, PCDD/F, NMVOC, PAH, B(k)F water usage and recycling; exposure to areas of high water stress Water stress Water stress (m 3 of H 2 O equivalents) 35 Water consumption air pollutants Eutrophication, freshwater Fraction of nutrients reaching freshwater end compartment (P) 52,53 NH 3 - air, P emissions to water; air pollutants Eutrophication, marine Fraction of nutrients reaching marine end compartment (kg N eq) 52,53 NH 3 , N the transition to a circular economy n/a Material footprint Material footprint (tonnes of cultivated biomass, extracted mineral ore and fossils) 35 Extraction Used pollution prevention and control air pollutants Acidification Accumulated Exceedance (mol H + eq) 52,53 SOx, NOx, NH 3 air pollutants Eutrophication, terrestrial Accumulated Exceedance (mol N eq) 52,53 NH 3 , NOx emissions of inorganic pollutants Human toxicity, cancer Comparative Toxic Unit for humans (CTUh) 52,53 Benzo(a)pyrene, PCDD_F, HCB, As, Cd, Hg, Ni, B(a)P, Pb, PCDD/F emissions of inorganic pollutants Human toxicity, non-cancer Comparative Toxic Unit for humans (CTUh) 52,53 HCB, As, Cd, Cu, Hg, Ni, Pb, Zn emissions of inorganic pollutants Particulate matter Impact on human health (DALYs) 35 PM 2.5 , CO, SOx, NH 3 , TSP air pollutants Photochemical ozone formation, human health Tropospheric ozone concentration increase (kg NMVOC eq) 52,53 CH 4 , SOx, CO, NMVOC the protection and restoration of biodiversity and ecosystems activities negatively affecting biodiversity-sensitive areas; natural species and protected areas; deforestation; land degradation, desertification, soil sealing Land-use related biodiversity loss (global m3 PDF years) 35 Land use, crop, forest, pasture For social issues, the SFDR proposes rather qualitative indicators, some even only concerning due diligence & compliance (for example, “monitoring compliance to OECD Guidelines for Multinational Enterprises”) instead of quantitative ones, focused on impact. We provide a set of rather quantitative impact indicators matching the SFDR’s PAIs, that are also linked to the social issues identified by the EU Taxonomy and the CSRD. To ensure that one can estimate impact at financial product level, we analyzed indicator availability from PSILCA 34, 29 , the social input-output life cycle assessment database which contains over 90 indicators. We selected a set of 13 social indicators (Table 2 ) related with the social indicators categories proposed by UNEP Social LCA Guidelines 23 . Yet, we did not consider the indicator values expressed in raw units, but their translation in so called “medium risk hours equivalents” (mrh), as proposed in the PSILCA documentation 34 . The raw indicator unit, while easier to interpret, cannot be extended easily to estimate life cycle impacts. For example, in the case of a raw unit in percentages, one cannot extend that to the estimation of life cycle impacts, as the percentage unit does not function like a physical unit, when the direct percentage is known. Medium risk hours equivalents unit is the multiplication of the hours worked in the sector with a factor that represents the extent of risk based on predefined criteria, where a medium risk has a factor 1. The mrh unit can be used to derive the life cycle impacts and allows for comparison between indicators. Table 2 13 social impact indicators for sustainable finance reporting, based on PSILCA. The indicators are compiled using two different literature sources. In the first column we list the EU Taxonomy environmental objectives. In the second column, we make the connection to SFDR PAIs. In the third column, we list the final set of 13 social indicators. EU Taxonomy minimum social safeguards Connected SFDR PAI Social indicator (raw unit from PSILCA database) Unit considered in the analysis corruption Cases of insufficient action taken to address breaches of standards of anti-corruption and antibribery Active involvement of enterprises in corruption (%) medium risk hours (mrh) fair competition Cases of insufficient action taken to address breaches of standards of anti-corruption and antibribery Presence of anti-competitive behaviour or violation of anti-trusted monopoly legislation (score of ordinal 0–3 scale) mrh human rights Operations and suppliers at significant risk of incidents of child labour Children in employment (%) mrh Unadjusted gender pay gap Gender wage gap (%) mrh Operations and suppliers at significant risk of incidents of forced or compulsory labour Frequency of forced labour (cases per 1,000 inhabitants in the country) mrh Lack of processes and measures for preventing trafficking in human beings; lack of due diligence Presence of sufficient safety measures (Cases per 100,000 employees) mrh Trafficking in persons (Trier) mrh Rate of accidents Rate of fatal accidents at workplace (#/yr and 100,000 employees) mrh Number of days lost to injuries, accidents, fatalities, or illness Rate of non-fatal accidents at workplace (#/yr and 100,000 employees) mrh Violations of UN Global Compact principles and Organisation for Economic Development (OECD) Guidelines for Multinational Enterprises (Respect for core labour standards - e.g., freedom of association and collective bargaining; non-discrimination in employment and occupation) Evidence of violations of laws and employment regulations (cases per 10,000 employees) mrh Right of Association mrh Right of Collective bargaining Right to Strike taxation n/a n/a n/a Methodological framework Input-output life cycle analysis (IOLCA) allows to estimate regional and sectorial impacts per monetary unit, making it suitable for assessments at organisation/company level, and has been previously used to this aim 29,57,58 . IOLCA is particularly suited, as at company level reliable and complete data is available currently only in monetary terms in the form of revenue streams. Moreover, this type of analysis is useful for the assessment of indirect or supply chain impacts, as companies do not have visibility over their indirect suppliers, even if it is usually where the majority of impacts take place 27 . Finally, IOLCA has been previously adapted to estimate impacts of financial portfolios, both in academia 24,29 , and in the development of proprietary models by different data providers 59,60 . At the level of financial portfolios, impacts from many companies have to be aggregated, and thus using IOLCA as a uniform method across all companies in a portfolio ensures consistency and additionality in the assessment process. Crucial in our framework is the consideration of IOLCA-based impacts as averages per country and sector, following an organizational LCA approach, and not per sectorial product, following a conventional product-oriented LCA approach, for which additional transformations are needed 61 . Although a consequential aim can be envisioned, trying to address what the consequences are of investing in a certain fund at the inventory level, there is a lack of a consequential IO-based life cycle database at sector-level. Hence, conventional attributional modelling has been applied, and can be regarded as an approximation. In the rest of this manuscript, we do not come back on this distinction between attributional versus consequential 62 . The step-by-step schematic representation of the IOLCA framework for assessment of impacts at financial product level is shown in Fig. 6 . Technically, the calculation of impact factors is based on the conventional input-output modelling using matrix calculation. In a first step (Module 1), we derived the IOLCA-based direct and indirect impact factors for the selected set of environmental and social impact indicators. In the case of environmental indicators, characterization factors (CF) are used as weighting proportions to define aggregated impact indicators, that group more environmental flows under the same impact category. Following the input-output nomenclature, the direct impact factors vector for each impact indicator contains impact factors for each country-sector combination and is obtained by dividing matrix \(F\) (total impacts by country-sector) by total output \(x\) : \(S=\frac{F}{x}\) (Eq. 1). Then, we have computed the total (life cycle) requirements matrix \(\left(L\right)\) , or the Leontief inverse, from the original input-output table \(\left(A\right)\) , the direct requirements matrix \(:\) \(L=(I-A{)}^{-1}\) (Eq. 2). The Leontief inverse allows us to compute the life cycle impact factors \(M\) (or “multipliers”), by multiplying matrices \(S\) and \(L\) : \(M=S \times L\) (Eq. 3). With the life cycle impact factors vector we are only capturing direct and supply chain impacts (or upstream impacts). Given the use of an IOLCA framework, the computation of downstream impacts (impacts from use phase onwards) it not straightforward and cannot be derived directly from the IOLCA tables. However, it could be estimated using traditional LCA data, but it is not covered in this paper. The S and M vectors, explained above, are used to derive the database of country-sector direct, indirect and life cycle impact factors \(\left(IF\right)\) , for each indicator and country-sector combination (impact factor by country \(j\) and sector \(k\) ) 13 . The \(IF\) s are then linked with the revenue breakdown for a company \(i\) ( \({R}_{ijk}\) ), as per Eq. 4 below. The quality and granularity of revenue-level data is a main driver of final reliability of company-level impact estimates. \({I}_{ci}=\sum _{j}\sum _{k} {IF}_{cjk} {R}_{ijk}\) (Eq. 4) The next key step is defining the sector and country level correspondence (concordance matrix) between the financial revenue database and the input-output database (Module 2). The concordance matrix bridges the IOLCA sector and country dictionary for impact factors with the FactSet company-level country and sector dictionary for revenue breakdown. A company will be assigned the average impact factors of the general country-sectors combinations that constitute its revenue generation streams – for example, Chemicals, Plastics and Other Manufacturing sectors in the US, China, and Germany for company BASF. This allows to obtain a life cycle impact per company (e.g., kg CO 2 eq. per euro output for GHG emissions indicator), that is the weighted average of the underlying economic activities of the company (Module 3). Finally, impacts at fund level \(\) can be expressed as absolute values in terms of owned impacts, for each impact indicator (category) \(c\) , by computing the share of a company’s impact that an investment fund \(f\) is responsible for (Module 4) 13 . The impact is derived based on investment fund-level information: the list of its public equity investments and the amount invested. Practically, for one impact indicator, the total impact of a company ( \({I}_{ci}\) ) is divided by the market value ( \({M}_{i})\) – total shares multiplied by price per share. Each shareholder is attributed its share of the holding, per monetary unit of investment. For each company, the weight held by the fund in the company is accounted for ( \({w}_{if}\) ), which is the invested amount by each fund in each of its company holdings. This measure accounts for the market valuation of a company, dividing the responsibility of impact between all its shareholders: \({Fund impact}_{cf}= \sum _{i}{w}_{if}\frac{{I}_{ci}}{{M}_{i}}\) (Eq. 5). Database selection and handling A suite of environmental and social input-output databases is available, each with distinct characteristics, but building on the same principles. Widely used databases for environmental assessments are EXIOBASE 28 , EORA 63 , GTAP and OECD 64 . The two main databases for social assessment are PSILCA 34 and the Social Hotspot Database (SHDB). While the environmental databases are fully free, or free for academic use, neither of the two social databases are freely available. Differences between databases are in the level of disaggregation available at country and sector level and in the impact extensions available. Deviations in data and results between input-output databases have been studied in previous work and the main drivers of variation are the structure of the economic flows and the environmental and social accounts data 65 . As such, our results and coverage of proposed indicators and underlying environmental flows are influenced by our choice of primary IO database. For the environmental analysis, we have chosen EXIOBASE, a IO database developed and maintained under a European research project 28 , which has been widely used in academia and in practical case studies for environmental assessments 28,66 . The choice of EXIOBASE has been previously detailed in the context of evaluating funds using IOLCA, but this was done solely for GHG emissions 24 . Compared to other input-output databases, it has a greater coverage of environmental accounts and a detailed coverage of European Union economies 64,65 (49 countries/regions, 163 sectors and 1,114 environmental flows). The EXIOBASE is used to extract direct and indirect impact factors for the 13 environmental impact indicators. Data is reported at environmental flow level. We then needed to aggregate multiple environmental flows to form impact indicators, using their specific characterization factors as weighting proportion. The CF are based on developments from the Product Environmental Footprint 3.1 guidance and/or referenced literature, depending on the impact indicator. For the matching between environmental IO database and financial revenue, we use the concordance matrix developed manually in Popescu et al. 24 , based on finding the best match between the FactSet RBICS database used and the EXIOBASE nomenclature. For the social assessment, we have chosen PSILCA, given the better accessibility and sector-level coverage. Social input-output databases have only been recently developed, to aid in accounting for the social impacts embodied in the global economy. Therefore, their reliability and use are lower. For the concordance matrix of the social database PSILCA and company-level data, the process was challenging, as there is not a common sector classification between countries in PSILCA. To ease the exercise, we have aggregated all the impacts at the level of the common 26 sectors classification (which is also the common classification of the EORA26 database), by computing the mean of the impact factors of all sectors linked to one EORA26 sector. For the final social concordance matrix, we linked the EORA26 classification to the sectorial classification from the database of financial revenue, in a 1-to-n linking – meaning that more country-sectors from the revenue database will receive the same impact factor, as they are part of the same aggregated EORA26 sector. As source for financial information, such as holding amount at fund level and company-level revenue data, we use the proprietary dataset of FactSet 67 , that can be accessed by purchasing a license. The datasets are FactSet Ownership, for investment fund-level data, and GeoRev and RBICS, for information on the distribution of company revenue at country and sector level. Having data in monetary amounts about the revenue distribution of each company is the best available option. Ideally, companies would share information in physical units about the produced amounts and purchased products. However, companies seldom disclose this type of information and the most reliable and complete data available for produced amounts is revenue-level data, in monetary units. Selection of funds’ sample According to market research by Morningstar 26 , SFDR-labelled article 8 and article 9 funds amounted to 10,608 in December 2022, representing 37.8% of all the funds available for sale in Europe. Our initial sample, of article 8 and 9 funds listed on the Luxembourg Green Exchange, is of 1,389 funds. The assets under management of these funds represent 13.7% of the total AuM of SFDR article 8 and 9 funds (i.e., 630.23 billion USD out of 5.01 trillion USD). The total global pool of sustainability-labelled funds, which comprises all types of funds, not only equity funds, is estimated at around 5 trillion, 12 times higher than our sample 26 . Our final sample is reduced to 230 funds, after removing funds with more asset classes (as it leads to double counting for impact intensity metrics) and removing non-equity funds (i.e., funds investing in fixed income or money market funds), as for these we cannot directly apply our proposed assessment model. The sample of equity funds is heterogenous in terms of investment theme and size, ranging from 4 million USD to 16.5 billion USD in Assets under Management (AuM). Spearman rank correlation We apply the Spearman rank correlation, as this is not sensitive to outliers and leads to more reliable results that the traditional, default correlation method used – the Pearson correlation. The Spearman rank correlation uses the rank of the observations on each variable, thus being described using a monotonic function 68 . Software and data availability The data estimation, analysis, and output were performed in python and Jupyter notebooks. 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Supplementary Files SIpapereusfindicatorsfinal.pdf SITable1Table2.xlsx Dataset 1 SIdatausedforFig5.xlsx Dataset 2 Cite Share Download PDF Status: Published Journal Publication published 29 Jun, 2024 Read the published version in Communications Earth & Environment → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3345219","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":239120839,"identity":"3b0d489a-c121-4df0-a1ef-ed297a55e815","order_by":0,"name":"Ioana Popescu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBACNlRuhQUPkGQ8QIKWMxI8ICG8WlABY5sEA0EtfOzNzx78YLgjZ87e/vAz7zwJGfn5DQyHefA5jOeYuWEPwzNjy54zxtK82yR4DI4xENAikWAGdP/hxA03ctiYwVrYCGpJ/yb5B6Tl/vNnzLxzJHjk2whqyTGThtjCYMbM2wC0kaDDeM6UScsYgPySYyw55xjIL4kNB+fg0SLf3r5N8k0FKMSOP/zwpsbGXr758MEHb/BogQCDAwwGCB5jA0ENDKC4MyCsaBSMglEwCkYqAAAuHUPWplfT9gAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-6965-6521","institution":"Luxembourg Institute of Science and Technology (LIST)","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ioana","middleName":"","lastName":"Popescu","suffix":""},{"id":239120840,"identity":"444192e3-95a0-4ff2-b4f8-57f9a2920085","order_by":1,"name":"Thomas Schaubroeck","email":"","orcid":"","institution":"Luxembourg Institute of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Schaubroeck","suffix":""},{"id":239120841,"identity":"8d5d75a9-6603-4508-8cb1-3013f954f1ad","order_by":2,"name":"Thomas Gibon","email":"","orcid":"https://orcid.org/0000-0002-2778-8825","institution":"Luxembourg Institute of Science and Technology (LIST)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Gibon","suffix":""},{"id":239120842,"identity":"69270e06-14f9-4267-9a17-eff4b1f8220f","order_by":3,"name":"Claudio Petucco","email":"","orcid":"https://orcid.org/0000-0002-2377-7678","institution":"Luxembourg Institute of Science and Technology (LIST)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Claudio","middleName":"","lastName":"Petucco","suffix":""},{"id":239120843,"identity":"a3fef821-a6d2-4309-99e0-3e39c86ba580","order_by":4,"name":"Enrico Benetto","email":"","orcid":"","institution":"Luxembourg Institute of Science and Technology (LIST)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Enrico","middleName":"","lastName":"Benetto","suffix":""}],"badges":[],"createdAt":"2023-09-11 14:00:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3345219/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3345219/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43247-024-01479-4","type":"published","date":"2024-06-29T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":44790110,"identity":"f82a96f8-39dc-4e13-9a59-fa33ac74150d","added_by":"auto","created_at":"2023-10-17 14:37:12","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1683942,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Environmental impact indicators A and (B) social impact indicators, under the EU sustainable finance regulations and proposed life-cycle-based ready-to-use indicators. The indicators/objectives that are in orange boxes are not directly matched between the regulations and the life-cycle-based indicators. The darker-colored SFDR PAI indicators refer to the mandatory ones.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3345219/v1/821d734818ff4eabe57cda22.jpeg"},{"id":44791529,"identity":"948570b6-b8e6-4458-ac31-8fb74d31ec92","added_by":"auto","created_at":"2023-10-17 14:45:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":370584,"visible":true,"origin":"","legend":"\u003cp\u003eStripplots showing the distribution of life cycle impact intensity of funds in the sample of year 2021 for fund-level holdings data and 2019 for company-level revenue data. The results are measured in respective impact unit per monetary amount of company revenue. Panel A shows results for the 13 environmental impact indicators, and Panel B, for the 13 social impact indicators.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3345219/v1/c8739df2aaf765b8a9cebb4d.png"},{"id":44792856,"identity":"d91ffd4a-11f6-4e66-b463-61319ccc0f60","added_by":"auto","created_at":"2023-10-17 14:53:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":181847,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman’s rank correlation coefficients between environmental and social impact indicators.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3345219/v1/bfcafa02588de40a835241b5.png"},{"id":44790108,"identity":"fb0c01b6-6dce-4e40-809b-c76309a23bb4","added_by":"auto","created_at":"2023-10-17 14:37:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":108135,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstimation of the total direct and indirect environmental and social impacts of a sample of sustainability-labeled funds and equivalence in impact of millions of EU citizens.\u003c/strong\u003e The stacked bars show the proportion between direct and indirect impacts, for A. 13 environmental indicators (with specific unit) and B. 13 social indicators (measured in medium worker hours across all indicators). The labels of the bars represent the actual values for the direct and indirect estimated sum of impacts for the sample of funds. The sample contains 230 sustainability-labeled funds. The impact at fund-holding level is computed for the year 2019. The red line shows the equivalent of the total estimated life cycle impacts in million EU citizens\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3345219/v1/32e4663a8b3450769621414b.png"},{"id":44792855,"identity":"734b9bdb-7d61-48a5-bf90-89729f4e9a61","added_by":"auto","created_at":"2023-10-17 14:53:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":202662,"visible":true,"origin":"","legend":"\u003cp\u003eIdentifying the holdings that drive 50% of the funds’ impact. Holdings are grouped by main industry classification.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3345219/v1/b507ef008c18592a6ec98e4b.png"},{"id":44790113,"identity":"fe559951-23b5-4d09-917c-3ce96f515d41","added_by":"auto","created_at":"2023-10-17 14:37:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":128520,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the procedure to ready-to-use life-cycle-based impact assessment for financial products.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3345219/v1/12fc142a103455199a9bbd17.png"},{"id":59469151,"identity":"c6617ede-116e-453a-8ce2-9921748ed0fe","added_by":"auto","created_at":"2024-07-02 07:16:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3306797,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3345219/v1/e2a7d331-6c79-4d6a-ac9b-05650067041c.pdf"},{"id":44790111,"identity":"bca5282e-f93f-4766-a654-3310af2f4684","added_by":"auto","created_at":"2023-10-17 14:37:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2389389,"visible":true,"origin":"","legend":"","description":"","filename":"SIpapereusfindicatorsfinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3345219/v1/09c2b2b23b68b257dc00fb7a.pdf"},{"id":44791527,"identity":"cbb4caab-abed-4b82-8084-464393f7db68","added_by":"auto","created_at":"2023-10-17 14:45:12","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":125748,"visible":true,"origin":"","legend":"Dataset 1","description":"","filename":"SITable1Table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3345219/v1/01ff38b6a9eb5788b4321098.xlsx"},{"id":44790114,"identity":"9beb3db5-573e-4851-97db-d0326bbf8ee5","added_by":"auto","created_at":"2023-10-17 14:37:12","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":105430,"visible":true,"origin":"","legend":"Dataset 2","description":"","filename":"SIdatausedforFig5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3345219/v1/4c2a829902875137c1587f54.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Investment funds are responsible for substantial environmental and social impacts with trade-offs","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMonetary flows towards so-called sustainable investment funds are projected to grow to one third of the global market by 2025 (53 trillion USD)\u003csup\u003e1\u003c/sup\u003e. However, the effectiveness of sustainable finance in driving changes in the real economy is heavily scrutinized\u003csup\u003e2,3\u003c/sup\u003e as there is no top-down, thorough assessment of their sustainability claims\u003csup\u003e4,5\u003c/sup\u003e, leading to an increased risk of greenwashing\u003csup\u003e6\u003c/sup\u003e. Nonetheless, this risk is being progressively compensated from a bottom-up perspective by the growing focus on measuring the sustainability of financial products such as investment funds. Investors\u0026rsquo; awareness is, among others, driven by the imminence of climate (but also other environmental and social) risks that can jeopardize the long-term profitability of investments\u003csup\u003e7\u003c/sup\u003e. Indeed, previous studies\u003csup\u003e7,8\u003c/sup\u003e highlighted the significant hidden exposure to losses in the financial markets due to climate change. Next to policy makers, investors are an additional lever to push for a sustainable economy, by exerting influence on the companies they invest in\u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHaving access to reliable and complete sustainability information is vital to the integration of sustainability in the investment decision making process\u003csup\u003e10,11\u003c/sup\u003e. Existing environmental, social and governance (ESG) ratings, widely used as proxy for sustainable performance, are mostly unreliable and do not offer sound quantitative information to investors\u003csup\u003e12,13\u003c/sup\u003e. To tackle this issue, initiatives to standardize sustainability assessment at financial product level are emerging globally\u003csup\u003e14\u003c/sup\u003e, with the regulations under the EU Sustainable Finance Action Plan being regarded as the most ambitious\u003csup\u003e15,16\u003c/sup\u003e. The Sustainable Finance Disclosure Regulation (SFDR)\u003csup\u003e17\u003c/sup\u003e is the piece of legislation addressed to financial institutions and is the driver of our research. However, indicators under the SFDR are not comprehensive enough, while at the same time reliable company data to aid in sustainability reporting is unavailable.\u003c/p\u003e \u003cp\u003eIn this paper, we demonstrate that science-based, ready-to-use, environmental and social indicators aligned with regulations can already be operationalized in a coherent framework for sustainability assessment of investment funds. To define the framework, we begin by analysing sustainable finance reporting regulations, identifying links with indicators from the best-practice sustainability assessment framework of life cycle assessment (LCA). As fostered to a considerable degree in regulations\u003csup\u003e18,19\u003c/sup\u003e, LCA can provide consistent estimation of sustainability impact at product and organisation level\u003csup\u003e13,20,21\u003c/sup\u003e. Adopting a life cycle perspective to impact assessment is crucial in order to avoid shifting of impact to indirect stages of production and consumption. Moreover, environmental LCA methodology includes a comprehensive set of impact indicators, to avoid trade-offs between these\u003csup\u003e13,22\u003c/sup\u003e. Importantly, social impact indicators can be retrieved using Social LCA\u003csup\u003e23\u003c/sup\u003e, which has not yet been broadly applied in this context.\u003c/p\u003e \u003cp\u003eAs a first contribution, we designate 13 environmental and 13 social life-cycle-based impact indicators that can be estimated at company and investment fund-level using input-output LCA (IOLCA)\u003csup\u003e25\u003c/sup\u003e (Methods, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In our innovative approach, we are linking two different input-output databases with financial information at company and investment fund level, and we extend the approach of Popescu et al.\u003csup\u003e24\u003c/sup\u003e, which focused on carbon footprint of investment funds, to others impact indicators, both environmental and social.\u003c/p\u003e \u003cp\u003eAs a second contribution, we apply this framework to a sample of 230 investment funds. The sample represents all self-labelled sustainable equity funds listed on the Luxembourg Green Exchange, for which complete information could be retrieved. This is the biggest exchange for green financial products, which represent more than 13% of the assets under management (AuM) of all funds self-labelled sustainable under the EU SFDR\u003csup\u003e26\u003c/sup\u003e (so-called article 8 and article 9 funds). To calculate impact at investment fund level, we first apply IOLCA analysis to estimate impact factors at country-sector level\u003csup\u003e27,28\u003c/sup\u003e, and then at company level, using the country and sector distribution of a company\u0026rsquo;s revenue. This data is then aggregated at investment fund level. This approach has been applied by ourselves and others in past work\u003csup\u003e24,29\u003c/sup\u003e. In the analysis of these results, we first study the spread between funds for each impact category, and the correlations between impact categories and within the group of social and the group of environmental indicators, to identify synergies, and trade-offs. Subsequently, we analyse the magnitude of impact per impact category, attributed to the funds\u0026rsquo; sample and we discuss the differences between direct and indirect share of impact. To better grasp the amplitude of impact attributable to funds, we compare it to the impact attributable to the consumption of all EU citizens over one year. As a final analysis, we look at the top funds holdings that drive the lion\u0026rsquo;s share of fund-level impact and discuss implications.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv\u003e\n\u003ch2\u003eA set of consistent and ready-to-use environmental and social impact indicators\u003c/h2\u003e\n\u003cp\u003eThe three main pieces of regulations that mandate sustainability-level disclosures are: the EU Sustainable Finance Disclosure Regulation\u003csup\u003e17\u003c/sup\u003e (SFDR), the EU Corporate Sustainability Reporting Directive (CSRD)\u003csup\u003e30\u003c/sup\u003e, and the EU Taxonomy\u003csup\u003e31\u003c/sup\u003e. In our framework development for indicators (Fig.\u0026nbsp;1), we first define the link between environmental and social objectives from the EU Taxonomy and then indicators proposed under SFDR (the so-called Principal Adverse Impact (PAI) indicators for investments in investee companies) and CSRD. We draw the (mis-)matchings between the sustainability indicators proposed. In a last step to define our framework, we link life-cycle-based indicators (last column in Fig.\u0026nbsp;1), that are ready-to-use and science-based, which could be thus used in reporting against the SFDR regulation (as further discussed in Methods and Supplementary Information - SI).\u003c/p\u003e\n\u003cp\u003eThe comprehensiveness and rigorousness of SFDR-proposed indicators is unsatisfactory, when compared to the state-of-the-art indicators in the sustainability assessment field. First, there is an inconsistent coverage of sustainability issues, as compared to widely accepted frameworks for sustainability assessment. Second, disclosure over the life cycle is not mandated. Finally, there is no clear methodology to underpin the indicators proposed, which may lead to reported data not being comparable between financial institutions (argumentation is further detailed in the SI).\u003c/p\u003e\n\u003cp\u003eThe EU environmental footprint (EF) method\u003csup\u003e32\u003c/sup\u003e and the UNEP Social LCA Guidelines\u003csup\u003e23\u003c/sup\u003e are chosen as guidance frameworks. These frameworks are among the most consensual at policy level and are relying on science-based sustainability assessment methods. The selection of ready-to-use, life-cycle-based indicators is restricted, in a first step, by the availability of raw environmental and social indicators in the IOLCA databases of choice. For environmental impacts, we use indicators that can be estimated using the environmentally extended multi-regional input-output database EXIOBASE, which was employed for similar purposes\u003csup\u003e29,33\u003c/sup\u003e and has a detailed country and sector-level coverage. For the social impact assessment, the PSILCA database\u003csup\u003e34\u003c/sup\u003e is used, which has a large coverage of social indicators and detailed country and sector-level coverage for EU countries (Methods).\u003c/p\u003e\n\u003cp\u003eLife-cycle-based ready-to-use indicators have been found for almost all regulation objectives/indicators. Concerning well-matching environmental impact topics, climate change mitigation is the only objective with a one-to-one relationship between all standards, validating the maturity level and consensus on this main environmental issue. For SFDR PAI indicator \u0026ldquo;emissions of air pollutants\u0026rdquo; (like ammonia \u0026ndash; NH\u003csub\u003e3\u003c/sub\u003e), the EF method provides several impact indicators to assess their effects \u0026ndash; namely terrestrial acidification and eutrophication\u003csup\u003e32\u003c/sup\u003e. Similarly, \u0026ldquo;emissions to water\u0026rdquo; and \u0026ldquo;emissions of inorganic pollutants\u0026rdquo; are represented by the corresponding life cycle impact indicators of \u003cem\u003etoxicity\u003c/em\u003e and \u003cem\u003eeutrophication\u003c/em\u003e. The SFDR PAIs of \u0026ldquo;land degradation\u0026rdquo; and \u0026ldquo;activities negatively affecting biodiversity-sensitive areas\u0026ldquo; can be grouped under the life cycle impact indicator of \u003cem\u003eland-use related biodiversity loss\u003c/em\u003e\u003csup\u003e35\u003c/sup\u003e. For \u0026ldquo;water use\u0026rdquo; and \u0026ldquo;exposure to areas of high water stress\u0026rdquo;, we propose the alternative indicator of \u003cem\u003ewater stress\u003c/em\u003e, which weights water usage based on the characteristics of the region where it takes place \u0026ndash; whether the area is more at risk of water stress or not\u003csup\u003e35\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAn exception is the EU taxonomy objective of climate change adaptation having no standardized equivalent indicator in LCA. In the extended literature, there are however many examples of indicators specifically developed to measure adaptation, e.g., flood safety levels for a stormwater management system\u003csup\u003e36\u003c/sup\u003e measuring the impact compared to a reference scenario. In some cases (e.g., electricity generation) the same indicators used for climate change mitigation can apply\u003csup\u003e18\u003c/sup\u003e. Similarly, for the \u0026ldquo;circular economy\u0026rdquo; (CE) objective, indicators measuring circularity do not directly represent environmental impacts, instead they are a proxy (or not) for better (or worse) environmental performances which shall anyway be measured separately. Multi-dimensional scoring tools that serve as CE indicators have been previously developed and tested, such as the Circularity Potential Indicator\u003csup\u003e37,38\u003c/sup\u003e. Life-cycle-based indicators assessing resource use and scarcity\u003csup\u003e39\u003c/sup\u003e may be used to this aim, until better indicators based on reliable collected data will be developed\u003csup\u003e38,40\u003c/sup\u003e. Social indicators from regulations tend to be qualitative (Fig.\u0026nbsp;1 Panel B). For example, the scope of the SFDR-proposed indicator of \u0026ldquo;violations of UN Global Compact principles and OECD Guidelines for Multinational Enterprises\u0026rdquo; is too broad and would not give stakeholders a sense of the social impacts that underline a funds\u0026rsquo; portfolio. While social impacts are by default driven by more abstract characteristics of a company \u0026ndash; such as employee policy \u0026ndash; more quantitative indicators can be developed, that allow for a clearer assessment of a company or investment. Hereto, quantitative indicators by social impact category are being developed in social LCA\u003csup\u003e23,41\u003c/sup\u003e. We propose semi-quantitative risk-based indicators available in the social IOLCA database PSILCA, that can be estimated at sector, company, and financial product level. Examples of PSILCA indicators are rate of accidents, children in employment, right to collective bargaining.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003ch2\u003eApplication to a representative sample of sustainable funds\u003c/h2\u003e\n\u003cp\u003eWe applied the indicators framework to a representative sample of 230 equity funds, self-labelled sustainable under the SFDR classification, investing collectively in over 4,800 unique public companies worldwide. The sample represents the unique universe of SFDR Article 8 and Article 9 funds listed on the Luxembourg Green Exchange. The funds in the selected sample hold together 401\u0026nbsp;billion USD (US dollar) of investments, which, if compared to the size of an economy, is approximately as large as the gross domestic product (GDP) of Denmark over a year (398.3 USD billion in 2021, according to the World Bank data\u003csup\u003e42\u003c/sup\u003e).\u003c/p\u003e\n\u003cdiv\u003e\n\u003ch2\u003eWidely spread impact distribution for the funds sample\u003c/h2\u003e\n\u003cp\u003eFor the environmental impact assessment, the results at fund level vary by indicator (Fig.\u0026nbsp;2), with a widely spread distribution of impacts among Article 8 and Article 9 funds. Acidification, eutrophication and ecotoxicity impact intensities have highest spreads (for example, direct freshwater ecotoxicity impact indicator mean across funds is of 19.2 thousand CTUe/MUSD, with a standard deviation of 43.2 thousand CTUe/MUSD), while for the climate change indicator we see a smaller interval for the results distribution (245 tCO\u003csub\u003e2\u003c/sub\u003e/MUSD mean, and standard deviation of 481 tCO\u003csub\u003e2\u003c/sub\u003e/MUSD). All values are displayed in SI Table S1. For the set of social impact indicators, the impact range is more concentrated, especially for the Article 9 funds. Yet, outliers appear across all impacts. These can be driven by investments in companies which have activities in countries with large relative impacts, or be skewed towards specific industries, thus leading to a much larger result than the sample mean. Finally, these results show that choice between different funds may be associated with big differences in sustainability impact.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003ch2\u003eSynergies \u0026amp; trade-offs between social and environmental impacts\u003c/h2\u003e\n\u003cp\u003eEnvironmental and social impacts at fund level are not always strongly and positively correlated, as shown in the matrix of Spearman\u0026rsquo;s rank correlation coefficients in Fig.\u0026nbsp;3, implying that an investment fund or a company can rank highest for one indicator, while scoring lower for other indicators. In general, we observe high correlations within each distinct subset of social and environmental indicators, and low correlations in between the two, meaning that there are larger trade-offs in between environmental and social indicators than within the environmental or social indicators\u0026rsquo; groups.\u003c/p\u003e\n\u003cp\u003eWithin the set of environmental indicators, some strong synergies are visible, whenever impact indicators are derived from common environmental flows. For example, acidification is highly correlated (coefficient larger than 0.9) with terrestrial eutrophication and photochemical ozone formation: expectedly so as ammonia emissions are contributing to all these three impact indicators. Human toxicity is correlated to ecotoxicity as heavy metals emissions are an important contributor to these impact indicators. At the opposite, for human toxicity we observe trade-off with water stress, given the low degree of similarity between the two impact indicators. Another trade-off observed at fund-level is for indicator climate change with land-use related biodiversity loss. Biodiversity loss would thus be a very important indicator to measure alongside climate change, in order to avoid causing more harm for biodiversity when investing with reduction of GHG emissions as main goal. Within the set of social indicators, there is a predominance of very high correlations.\u003c/p\u003e\n\u003cp\u003eAt fund level, both the portfolio allocation and the sector-country distribution of the held companies\u0026rsquo; revenue drive the correlation coefficients. Given the fact that funds tend to have high similitude in portfolio allocation \u0026ndash; if, for example, more funds follow the market index \u0026ndash; correlation is higher at fund level. If we do the exercise at company-level, the inherent economic activities of the company drive the correlations. In the Supplementary Information Figure S3, we show correlation matrixes between all impact indicators at company level, separately by main sector group. For Retail and Wholesale Trade sectors, there are strong negative correlation coefficients between most of social and environmental categories, signalling a high trade-off when investing in these industries. Indeed, the trade sector can be described as having low environmental pressures, while having a high impact on workforce. On the contrary, for Transportation and Utilities companies, we see a weaker negative relation between social and environmental indicators, while we observe more pronounced negative correlations between environmental indicators \u0026ndash; especially between particulate matter and the other indicators, as companies with very high particulate matter impacts rank lower on other environmental indicators. Companies in the Mining industry are perhaps the most interesting, as they show strong trade-offs between social and environmental indicators, but also within environmental indicators (namely material footprint vs. the rest of environmental indicators).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003ch2\u003eMagnitude of direct and indirect impacts at fund level\u003c/h2\u003e\n\u003cp\u003eWe estimate the total life cycle impact of the funds sample, based on the amounts invested, and plot the proportion of direct and indirect impacts (covering the upstream life cycle chain) alongside absolute direct and indirect impacts (Fig.\u0026nbsp;4). For all indicators, indirect impacts are considerably larger than direct impacts. However, results vary by impact indicator. Indirect proportion is lower for particulate matter, photochemical ozone formation and GHG emissions, where the contribution of direct impacts to the total life cycle impact is higher than 40%. Similar contribution is observed among the social indicators, namely for lack of rights of association and rights to strike.\u003c/p\u003e\n\u003cp\u003eTo contextualize the estimated impacts of the funds sample, we express this in the population equivalents of the impact of EU citizens (assuming a EU population of 447\u0026nbsp;million citizens in 2019), based on the categories of the EU final demand in EXIOBASE (household and government consumption, inter alia). The funds\u0026rsquo; investments are equivalent to the impacts of between 2.1 to 28.2\u0026nbsp;million EU citizens, depending on the impact indicator chosen.\u003c/p\u003e\n\u003cp\u003eFor example, for climate change mitigation, the total sample of investment funds is responsible for 62.2\u0026nbsp;million tons of CO\u003csub\u003e2\u003c/sub\u003e-equivalents (MtCO\u003csub\u003e2\u003c/sub\u003e-eq) direct emissions and 70.9 MtCO\u003csub\u003e2\u003c/sub\u003e-eq indirect emissions. This is equivalent to the life cycle climate change impact attributable to the final consumption of Belgium in 2019 (11.5\u0026nbsp;million inhabitants), corresponding to 146.4 MtCO\u003csub\u003e2\u003c/sub\u003e-eq, based on input-output calculations.\u003c/p\u003e\n\u003cp\u003eThe variation in the million EU citizen equivalents is explained by the different drivers of impact for final demand versus funds\u0026rsquo; holdings. First, the investment pool of a fund investing in global public companies tends to be skewed towards companies from Finance, Services and Tech industries, as these are the companies with largest market valuation and largest share in the capital markets\u003csup\u003e24\u003c/sup\u003e. Companies in these industries generally have low direct environmental burdens (for finance companies, second-order impacts, via their investments, are not conventionally counted via the life-cycle-based method), compared to consumption goods, which may play a larger role in the final demand attributable to EU citizens, hence the lower citizen amount equivalent in terms of environmental impacts. Second, for some environmental and social categories, we expect European consumption to be more intensive than an average sample of global public companies (as one could describe the funds\u0026rsquo; holdings). For example, for the social indicators of trafficking in persons, restricted right to strike and restricted rights of association, the sample of funds has 20 times higher impacts than the total footprint of all EU citizens.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003ch2\u003eConcentration of impact in key industries and large companies\u003c/h2\u003e\n\u003cp\u003eWe identify companies (grouped by main industry) that drive the lion\u0026rsquo;s share of impact, by impact category. These companies drive in aggregate more than 50% of the total life cycle impacts estimated for the funds sample. In Fig.\u0026nbsp;5, we show results only for a handful of impact indicators, with all the other results in the SI (including listing of top companies in Supplementary Dataset 1). Fund-level allocation seems concentrated around a very small number of large corporations, meaning that the studied funds tend to hold similar large companies in their portfolios, with different holding amounts. The list of companies at the top varies depending on the impact category analyzed.\u003c/p\u003e\n\u003cp\u003eFor water stress indicator, 50% of the life cycle impact can be traced back to only 27 large corporations (out of almost 5,000 different companies). Funds hold aggregated positions amounting to 28\u0026nbsp;billion USD in these 27 companies (representing only 8.3% of all positions held). The main positions driving impact being investments in Nestl\u0026eacute;, Unilever, and Danone \u0026ndash; all three large companies from the fast-moving consumer goods (FMCG) sector. It is expected to see FMCG companies to bear the largest share, as these depend on manufacturing of diverse products, but also cultivation and processing of raw materials, in the case of the food processing sub-sector. Similar importance of FMCG companies is seen for land-use related biodiversity loss indicator, while companies in the Paper sector also play a large role here (due to deforestation impacts).\u003c/p\u003e\n\u003cp\u003eFor human toxicity impact indicator, companies in the Information Technology sector (IT) have, in aggregate, the highest contribution (largest companies being Schneider Electric SE, Samsung Electronics, and Siemens AG). IT companies, including semiconductor manufacturers, have large market values, hence the large exposure of funds. The high values for human toxicity are driven by the need of metals and other chemical compounds in the manufacturing phase.\u003c/p\u003e\n\u003cp\u003eFor climate change impacts, companies from the Utilities and Oil \u0026amp; Gas sectors drive the largest share of impact (biggest contributors Enel SpA, China Petroleum, and Iberdrola SA).\u003c/p\u003e\n\u003cp\u003eFor social impacts, there is a more even distribution of impacts between industries invested in. Surprisingly different to the environmental impacts is the prevalence of Finance and Services sector companies as high contributors to negative social impacts. This is because social issues tend to be more prevalent in finance and services-related sectors. Moreover, for indicators anti-competitive behavior and children in employment, companies from the Industrials sector also have a very high contribution.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe literature on sustainability assessment of investment products is scarce, focused on GHG emissions\u003csup\u003e24,29,43\u003c/sup\u003e, despite the call for better alignment of capital markets with sustainability goals beyond reduction of carbon emissions\u003csup\u003e44\u003c/sup\u003e. Previous literature has assessed the climate performance of investment funds\u003csup\u003e45\u003c/sup\u003e and the exposure to climate risk\u003csup\u003e7,8\u003c/sup\u003e, but has not analysed in parallel multiple environmental and social impact indicators. In addition to previous literature studying connections between EU Taxonomy and LCA\u003csup\u003e19\u003c/sup\u003e, we link specific indicators from EU SFDR requirements with ready-to-use life-cycle-based indicators.\u003c/p\u003e \u003cp\u003eIt is clear from our results that trade-offs between and within social and environmental impact categories occur. Focusing on one or a few impact indicators in the detriment of others, could lead to doing more harm than good. As stipulated by the EU Taxonomy, impact assessment should include both environmental and social considerations, in order to avoid impact shifting within environmental categories (for example, green electricity can lead to reduction in GHG emissions but may cause negative impact on biodiversity) or missing impacts on categories of stakeholders not considered when looking at social issues (for example violating the rights of indigenous people by approving construction/deforestation on native lands). When looking at company-level correlations in terms of impacts, we see more specific trade-offs. For example, investments in Utilities companies have better scores on social issues and worse score on environmental issues. Our results strengthen previous findings that climate change cannot be used as a proxy for all environmental impacts\u003csup\u003e46\u003c/sup\u003e contributing to the debate on indicator proxies\u003csup\u003e47,48\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCompiling a full set of ready-to-use indicators is delimited by the availability of input-output databases with associate impact assessment methods of adequate quality. IO databases, like EXIOBASE and PSILCA, and LCA methodology in general are under continuous improvement and future developments will likely lead to more accurate and complete estimates resulting from our proposed framework. Our modelled results are susceptible to uncertainty coming, among else, from the limited level of detail in revenue reporting \u0026ndash; the coarser the level of reporting, the higher the risk to have under- or overestimated impacts. For example, this is the case for the company Iberdrola SA, where information on the type of electricity produced is not available in FactSet, leading to overestimation of impact for climate change indicator, as average electricity generation impact factors are then alternatively considered. Therefore, better reporting from the company side at the level of economic activities undertaken is necessary. In parallel, sustainability reporting requirements at investment level should develop to include more measurements of additionality and contribution, in addition to intensity metrics, in order to account for the transition plans of companies.\u003c/p\u003e \u003cp\u003eOur results provide evidence that impacts attributable to funds are substantial. The large share of indirect impacts, previously highlighted at industry level\u003csup\u003e27\u003c/sup\u003e, is also to be observed at fund level. Moreover, if we regard investment funds as entities carrying the responsibility for their investment, their environmental and social footprints are comparable to that of EU consumers, albeit much larger given the high value of capital markets. Hence, we call upon a stricter approach to disclosure requirements in terms of indirect impacts. For example, measuring and setting indirect impact targets allows investment managers to exert influence over the companies in the supply chain, thus increasing the potential engagement opportunities\u003csup\u003e9\u003c/sup\u003e. In addition, we have observed a strong concentration of large publicly listed companies in the portfolios of analyzed funds. Depending on the impact category, the industry and companies contributing most impact are shifting. The large exposure to certain companies and impact hotspots can be a driver of engagement with companies, demanding improvements in environmental and social practices\u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur analysis can serve as a baseline for harmonizing sustainable finance regulations and science-based sustainability assessment and its impact indicators. Standardization would facilitate comparability and reliability of indicators. Main strength of our approach is that impacts are estimated using the same background methodology data for all environmental indicators; for social indicators, similar methodology but a different underlying input-output database are used. Irrespective of the type of reporting requirements or their location, our proposed set of indicators is embedded in international practice related to sustainability assessment and can thus serve as a general framework for sustainability assessment at financial product level.\u003c/p\u003e \u003cp\u003eWhile outside the scope of the current paper, we acknowledge the importance of assessing the state of governance, at investment fund and company level. A robust governance policy at entity level would ensure the implementation of policies and activities that are helping advance the environmental and social agenda. A long-term perspective in value creation for stakeholders\u003csup\u003e49\u003c/sup\u003e is in harmony with pursuing environmental and social objectives that usually have a much larger time frame to materialize than financial objectives\u003csup\u003e50\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eSelection of life-cycle-based indicators\u003c/h2\u003e\n\u003cp\u003eAs starting point for proposal of the 26 environmental and social indicators, we had the so-called Principal Adverse Impacts (PAIs) proposed under the Sustainable Finance Disclosure Regulation (SFDR), which are defined under the SFDR\u0026rsquo;s Regulatory Technical Standards (RTS). As shown in SI Figure S2, we have divided the SFDR PAI indicators between inventory (specific environmental flow) and impact indicators (translation of effect of environmental flows on a certain impact type). Furthermore, we looked at how the SFDR indicators match with the six environmental objectives proposed under the EU Taxonomy and its Minimum Social Safeguards. Afterwards, we analysed the Corporate Sustainability Reporting Directive (CSRD), which, via its European Sustainability Reporting Standards (ESRS), makes reference to science-based measurement methods and standards on greenhouse gases emissions\u003csup\u003e51\u003c/sup\u003e, such as ISO:14046-1:2018, and on other environmental indicators, as the Environmental Footprint methods\u003csup\u003e32\u003c/sup\u003e (EF). More detailed analysis of the legislative framework around EU sustainable finance reporting can be found in the SI and in the tables of Supplementary Dataset 2.\u003c/p\u003e\n\u003cp\u003eAfterwards, we have considered current state-of-the-art in terms of life-cycle-based indicators. Especially for the environmental dimension, the difficulty in choosing a sufficient and comprehensive set of indicators, lies in the existence of multiple methods to assess the same impact category, even for established impact categories, like climate change. We preselected the Environmental Footprint (EF) version 3.1, updated in 2022 is proposing a set of 16 impact indicators and underlying methods and characterization factors, as (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) it is based on latest literature developments, (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) can be linked to ready-to-use indicators built using EXIOBASE\u003csup\u003e52,53\u003c/sup\u003e (the selected environmentally extended input-output database) and (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e) it is brought forward by EU policy. From these 16, we only selected 13 environmental indicators (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), with exclusion of two EF indicators that are not covered in the IOLCA data (ozone depletion and ionizing radiation), and the summing of two resource use indicators into represent the material footprint indicator. While the EF is considerably up to date in terms of impact indicator assessment methods, it does not integrate aspects like the difference in impact magnitude caused by the location where the impact takes place, which is highly relevant, especially for water stress\u003csup\u003e54\u003c/sup\u003e, pollution\u003csup\u003e55\u003c/sup\u003e, toxicity or biodiversity. Therefore, for water use, particulate matter, and land-use impact indicators in the EF methods, we use impact factors corresponding to location-specific impact indicators of water stress, particulate matter, and land-use related biodiversity loss, that are built using EXIOBASE. These are obtained from the work of Cabernard et al.\u003csup\u003e35\u003c/sup\u003e. The set of life-cycle-based indicators had to be further reduced because of missing coverage of environmental flows in the input-output database chosen. The missing coverage has been previously discussed in literature and input-output analyses would benefit in the future from the inclusion of other key environmental flows\u003csup\u003e52\u003c/sup\u003e, such as different toxic substances\u003csup\u003e52\u003c/sup\u003e, or pesticides\u003csup\u003e56\u003c/sup\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u003cem\u003e13 environmental impact indicators for sustainable finance reporting, based on Environmental Footprint version 3.1. and EXIOBASE. In the first column we list the EU Taxonomy environmental objectives. In the second column, we make the connection to SFDR PAIs. In the third column, we list the final set of 13 environmental indicators. In the last column we describe the environmental flows that are included to compute an impact indicator. An impact indicator is the sum of the impacts resulting from the environmental flows, calculated by multiplying each one of these with its corresponding characterization factor (i.e., the impact per unit of environmental flow estimated using different impact assessment models).\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEU Taxonomy Objective\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSFDR PAI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eProposed EF impact category\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUnit (environmental impact category indicator)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSource\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIncluded environmental flows\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eclimate change mitigation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGHG emissions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClimate change, total\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGHG emissions, GWP100 (kgCO\u003csub\u003e2\u003c/sub\u003e eq)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e52,53\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e, N\u003csub\u003e2\u003c/sub\u003eO, SF\u003csub\u003e6\u003c/sub\u003e, HFC, PFC\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eclimate change adaptation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en/a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en/a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en/a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en/a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en/a\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ethe sustainable use and protection of water and marine resources\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eemissions to water; inorganic pollutants\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEcotoxicity, freshwater\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eComparative Toxic Unit for ecosystems (CTUe)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e52,53\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBenzo(a)pyrene, Indeno(1,2,3-cd)pyrene, PCDD_F, HCB, As, Cd, Cr, Hg, Cu, Ni, Pb, Benzo(k)fluoranthene, Se, Zn, B(a)P, Indeno, PCDD/F, NMVOC, PAH, B(k)F\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ewater usage and recycling; exposure to areas of high water stress\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWater stress\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWater stress (m\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eof H\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eO equivalents)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e35\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWater consumption\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eair pollutants\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEutrophication, freshwater\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFraction of nutrients reaching freshwater end compartment (P)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e52,53\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e - air, P\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eemissions to water; air pollutants\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEutrophication, marine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFraction of nutrients reaching marine end compartment (kg N eq)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e52,53\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e, N\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ethe transition to a circular economy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en/a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaterial footprint\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eMaterial footprint (tonnes of cultivated biomass, extracted mineral ore and fossils)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e35\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExtraction Used\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003epollution prevention and control\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eair pollutants\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAcidification\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAccumulated Exceedance (mol H\u0026thinsp;+\u0026thinsp;eq)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e52,53\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSOx, NOx, NH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eair pollutants\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEutrophication, terrestrial\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAccumulated Exceedance (mol N eq)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e52,53\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e, NOx\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eemissions of inorganic pollutants\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHuman toxicity, cancer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eComparative Toxic Unit for humans (CTUh)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e52,53\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBenzo(a)pyrene, PCDD_F, HCB, As, Cd, Hg, Ni, B(a)P, Pb, PCDD/F\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eemissions of inorganic pollutants\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHuman toxicity, non-cancer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eComparative Toxic Unit for humans (CTUh)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e52,53\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHCB, As, Cd, Cu, Hg, Ni, Pb, Zn\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eemissions of inorganic pollutants\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eParticulate matter\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eImpact on human health (DALYs)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e35\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e, CO, SOx, NH\u003csub\u003e3\u003c/sub\u003e, TSP\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eair pollutants\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhotochemical ozone formation, human health\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTropospheric ozone concentration increase (kg NMVOC eq)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e52,53\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e, SOx, CO, NMVOC\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ethe protection and restoration of biodiversity and ecosystems\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eactivities negatively affecting biodiversity-sensitive areas; natural species and protected areas; deforestation; land degradation, desertification, soil sealing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLand-use related biodiversity loss\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(global m3 PDF years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e35\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLand use, crop, forest, pasture\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003eFor social issues, the SFDR proposes rather qualitative indicators, some even only concerning due diligence \u0026amp; compliance (for example, \u0026ldquo;monitoring compliance to OECD Guidelines for Multinational Enterprises\u0026rdquo;) instead of quantitative ones, focused on impact. We provide a set of rather quantitative impact indicators matching the SFDR\u0026rsquo;s PAIs, that are also linked to the social issues identified by the EU Taxonomy and the CSRD. To ensure that one can estimate impact at financial product level, we analyzed indicator availability from PSILCA\u003csup\u003e34, 29\u003c/sup\u003e, the social input-output life cycle assessment database which contains over 90 indicators. We selected a set of 13 social indicators (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) related with the social indicators categories proposed by UNEP Social LCA Guidelines\u003csup\u003e23\u003c/sup\u003e. Yet, we did not consider the indicator values expressed in raw units, but their translation in so called \u0026ldquo;medium risk hours equivalents\u0026rdquo; (mrh), as proposed in the PSILCA documentation\u003csup\u003e34\u003c/sup\u003e. The raw indicator unit, while easier to interpret, cannot be extended easily to estimate life cycle impacts. For example, in the case of a raw unit in percentages, one cannot extend that to the estimation of life cycle impacts, as the percentage unit does not function like a physical unit, when the direct percentage is known. Medium risk hours equivalents unit is the multiplication of the hours worked in the sector with a factor that represents the extent of risk based on predefined criteria, where a medium risk has a factor 1. The mrh unit can be used to derive the life cycle impacts and allows for comparison between indicators.\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e13 social impact indicators for sustainable finance reporting, based on PSILCA. The indicators are compiled using two different literature sources. In the first column we list the EU Taxonomy environmental objectives. In the second column, we make the connection to SFDR PAIs. In the third column, we list the final set of 13 social indicators.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eEU Taxonomy minimum social safeguards\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eConnected SFDR PAI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSocial indicator (raw unit from PSILCA database)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUnit considered in the analysis\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ecorruption\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCases of insufficient action taken to address breaches of standards of anti-corruption and antibribery\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eActive involvement of enterprises in corruption (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emedium risk hours (mrh)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003efair competition\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCases of insufficient action taken to address breaches of standards of anti-corruption and antibribery\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePresence of anti-competitive behaviour or violation of anti-trusted monopoly legislation (score of ordinal 0\u0026ndash;3 scale)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emrh\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" rowspan=\"11\" align=\"left\"\u003e\n\u003cp\u003ehuman rights\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOperations and suppliers at significant risk of incidents of child labour\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChildren in employment (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emrh\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnadjusted gender pay gap\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender wage gap (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emrh\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOperations and suppliers at significant risk of incidents of forced or compulsory labour\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrequency of forced labour (cases per 1,000 inhabitants in the country)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emrh\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eLack of processes and measures for preventing trafficking in human beings; lack of due diligence\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePresence of sufficient safety measures (Cases per 100,000 employees)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emrh\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTrafficking in persons (Trier)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emrh\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRate of accidents\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRate of fatal accidents at workplace (#/yr and 100,000 employees)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emrh\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of days lost to injuries, accidents, fatalities, or illness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRate of non-fatal accidents at workplace (#/yr and 100,000 employees)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emrh\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eViolations of UN Global Compact principles and Organisation for Economic Development (OECD) Guidelines for Multinational Enterprises (Respect for core labour standards - e.g., freedom of association and collective bargaining; non-discrimination in employment and occupation)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEvidence of violations of laws and employment regulations (cases per 10,000 employees)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emrh\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRight of Association\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003emrh\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRight of Collective bargaining\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRight to Strike\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003etaxation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003en/a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en/a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en/a\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eMethodological framework\u003c/h2\u003e\n\u003cp\u003eInput-output life cycle analysis (IOLCA) allows to estimate regional and sectorial impacts per monetary unit, making it suitable for assessments at organisation/company level, and has been previously used to this aim\u003csup\u003e29,57,58\u003c/sup\u003e. IOLCA is particularly suited, as at company level reliable and complete data is available currently only in monetary terms in the form of revenue streams. Moreover, this type of analysis is useful for the assessment of indirect or supply chain impacts, as companies do not have visibility over their indirect suppliers, even if it is usually where the majority of impacts take place\u003csup\u003e27\u003c/sup\u003e. Finally, IOLCA has been previously adapted to estimate impacts of financial portfolios, both in academia\u003csup\u003e24,29\u003c/sup\u003e, and in the development of proprietary models by different data providers\u003csup\u003e59,60\u003c/sup\u003e. At the level of financial portfolios, impacts from many companies have to be aggregated, and thus using IOLCA as a uniform method across all companies in a portfolio ensures consistency and additionality in the assessment process.\u003c/p\u003e\n\u003cp\u003eCrucial in our framework is the consideration of IOLCA-based impacts as averages per country and sector, following an organizational LCA approach, and not per sectorial product, following a conventional product-oriented LCA approach, for which additional transformations are needed\u003csup\u003e61\u003c/sup\u003e. Although a consequential aim can be envisioned, trying to address what the consequences are of investing in a certain fund at the inventory level, there is a lack of a consequential IO-based life cycle database at sector-level. Hence, conventional attributional modelling has been applied, and can be regarded as an approximation. In the rest of this manuscript, we do not come back on this distinction between attributional versus consequential\u003csup\u003e62\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe step-by-step schematic representation of the IOLCA framework for assessment of impacts at financial product level is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. Technically, the calculation of impact factors is based on the conventional input-output modelling using matrix calculation. In a first step (Module 1), we derived the IOLCA-based direct and indirect impact factors for the selected set of environmental and social impact indicators. In the case of environmental indicators, characterization factors (CF) are used as weighting proportions to define aggregated impact indicators, that group more environmental flows under the same impact category. Following the input-output nomenclature, the direct impact factors vector for each impact indicator contains impact factors for each country-sector combination and is obtained by dividing matrix \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(F\\)\u003c/span\u003e\u003c/span\u003e (total impacts by country-sector) by total output \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(x\\)\u003c/span\u003e\u003c/span\u003e:\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(S=\\frac{F}{x}\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;1).\u003c/p\u003e\n\u003cp\u003eThen, we have computed the total (life cycle) requirements matrix \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(L\\right)\\)\u003c/span\u003e\u003c/span\u003e, or the Leontief inverse, from the original input-output table \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(A\\right)\\)\u003c/span\u003e\u003c/span\u003e, the direct requirements matrix\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(L=(I-A{)}^{-1}\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;2).\u003c/p\u003e\n\u003cp\u003eThe Leontief inverse allows us to compute the life cycle impact factors \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(M\\)\u003c/span\u003e\u003c/span\u003e (or \u0026ldquo;multipliers\u0026rdquo;), by multiplying matrices \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(S\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(L\\)\u003c/span\u003e\u003c/span\u003e :\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(M=S \\times L\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;3).\u003c/p\u003e\n\u003cp\u003eWith the life cycle impact factors vector we are only capturing direct and supply chain impacts (or upstream impacts). Given the use of an IOLCA framework, the computation of downstream impacts (impacts from use phase onwards) it not straightforward and cannot be derived directly from the IOLCA tables. However, it could be estimated using traditional LCA data, but it is not covered in this paper. The S and M vectors, explained above, are used to derive the database of country-sector direct, indirect and life cycle impact factors \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(IF\\right)\\)\u003c/span\u003e\u003c/span\u003e, for each indicator and country-sector combination (impact factor by country \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003e and sector \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(k\\)\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e13\u003c/sup\u003e. The \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(IF\\)\u003c/span\u003e\u003c/span\u003es are then linked with the revenue breakdown for a company \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}_{ijk}\\)\u003c/span\u003e\u003c/span\u003e), as per Eq.\u0026nbsp;4 below. The quality and granularity of revenue-level data is a main driver of final reliability of company-level impact estimates.\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({I}_{ci}=\\sum _{j}\\sum _{k} {IF}_{cjk} {R}_{ijk}\\)\u003c/span\u003e \u003c/span\u003e(Eq.\u0026nbsp;4)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe next key step is defining the sector and country level correspondence (concordance matrix) between the financial revenue database and the input-output database (Module 2). The concordance matrix bridges the IOLCA sector and country dictionary for impact factors with the FactSet company-level country and sector dictionary for revenue breakdown. A company will be assigned the average impact factors of the general country-sectors combinations that constitute its revenue generation streams \u0026ndash; for example, Chemicals, Plastics and Other Manufacturing sectors in the US, China, and Germany for company BASF. This allows to obtain a life cycle impact per company (e.g., kg CO\u003csub\u003e2\u003c/sub\u003e eq.\u0026nbsp;per euro output for GHG emissions indicator), that is the weighted average of the underlying economic activities of the company (Module 3).\u003c/p\u003e\n\u003cp\u003eFinally, impacts at fund level\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\)\u003c/span\u003e\u003c/span\u003ecan be expressed as absolute values in terms of owned impacts, for each impact indicator (category) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(c\\)\u003c/span\u003e\u003c/span\u003e, by computing the share of a company\u0026rsquo;s impact that an investment fund \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(f\\)\u003c/span\u003e\u003c/span\u003e is responsible for (Module 4)\u003csup\u003e13\u003c/sup\u003e. The impact is derived based on investment fund-level information: the list of its public equity investments and the amount invested. Practically, for one impact indicator, the total impact of a company (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({I}_{ci}\\)\u003c/span\u003e\u003c/span\u003e) is divided by the market value (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({M}_{i})\\)\u003c/span\u003e\u003c/span\u003e \u0026ndash; total shares multiplied by price per share. Each shareholder is attributed its share of the holding, per monetary unit of investment. For each company, the weight held by the fund in the company is accounted for (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{if}\\)\u003c/span\u003e\u003c/span\u003e), which is the invested amount by each fund in each of its company holdings. This measure accounts for the market valuation of a company, dividing the responsibility of impact between all its shareholders:\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({Fund impact}_{cf}= \\sum _{i}{w}_{if}\\frac{{I}_{ci}}{{M}_{i}}\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;5).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eDatabase selection and handling\u003c/h2\u003e\n\u003cp\u003eA suite of environmental and social input-output databases is available, each with distinct characteristics, but building on the same principles. Widely used databases for environmental assessments are EXIOBASE\u003csup\u003e28\u003c/sup\u003e, EORA\u003csup\u003e63\u003c/sup\u003e, GTAP and OECD\u003csup\u003e64\u003c/sup\u003e. The two main databases for social assessment are PSILCA\u003csup\u003e34\u003c/sup\u003e and the Social Hotspot Database (SHDB). While the environmental databases are fully free, or free for academic use, neither of the two social databases are freely available. Differences between databases are in the level of disaggregation available at country and sector level and in the impact extensions available. Deviations in data and results between input-output databases have been studied in previous work and the main drivers of variation are the structure of the economic flows and the environmental and social accounts data\u003csup\u003e65\u003c/sup\u003e. As such, our results and coverage of proposed indicators and underlying environmental flows are influenced by our choice of primary IO database.\u003c/p\u003e\n\u003cp\u003eFor the environmental analysis, we have chosen EXIOBASE, a IO database developed and maintained under a European research project\u003csup\u003e28\u003c/sup\u003e, which has been widely used in academia and in practical case studies for environmental assessments\u003csup\u003e28,66\u003c/sup\u003e. The choice of EXIOBASE has been previously detailed in the context of evaluating funds using IOLCA, but this was done solely for GHG emissions\u003csup\u003e24\u003c/sup\u003e. Compared to other input-output databases, it has a greater coverage of environmental accounts and a detailed coverage of European Union economies\u003csup\u003e64,65\u003c/sup\u003e (49 countries/regions, 163 sectors and 1,114 environmental flows). The EXIOBASE is used to extract direct and indirect impact factors for the 13 environmental impact indicators. Data is reported at environmental flow level. We then needed to aggregate multiple environmental flows to form impact indicators, using their specific characterization factors as weighting proportion. The CF are based on developments from the Product Environmental Footprint 3.1 guidance and/or referenced literature, depending on the impact indicator. For the matching between environmental IO database and financial revenue, we use the concordance matrix developed manually in Popescu et al.\u003csup\u003e24\u003c/sup\u003e, based on finding the best match between the FactSet RBICS database used and the EXIOBASE nomenclature.\u003c/p\u003e\n\u003cp\u003eFor the social assessment, we have chosen PSILCA, given the better accessibility and sector-level coverage. Social input-output databases have only been recently developed, to aid in accounting for the social impacts embodied in the global economy. Therefore, their reliability and use are lower. For the concordance matrix of the social database PSILCA and company-level data, the process was challenging, as there is not a common sector classification between countries in PSILCA. To ease the exercise, we have aggregated all the impacts at the level of the common 26 sectors classification (which is also the common classification of the EORA26 database), by computing the mean of the impact factors of all sectors linked to one EORA26 sector. For the final social concordance matrix, we linked the EORA26 classification to the sectorial classification from the database of financial revenue, in a 1-to-n linking \u0026ndash; meaning that more country-sectors from the revenue database will receive the same impact factor, as they are part of the same aggregated EORA26 sector.\u003c/p\u003e\n\u003cp\u003eAs source for financial information, such as holding amount at fund level and company-level revenue data, we use the proprietary dataset of FactSet\u003csup\u003e67\u003c/sup\u003e, that can be accessed by purchasing a license. The datasets are FactSet Ownership, for investment fund-level data, and GeoRev and RBICS, for information on the distribution of company revenue at country and sector level. Having data in monetary amounts about the revenue distribution of each company is the best available option. Ideally, companies would share information in physical units about the produced amounts and purchased products. However, companies seldom disclose this type of information and the most reliable and complete data available for produced amounts is revenue-level data, in monetary units.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003eSelection of funds\u0026rsquo; sample\u003c/h2\u003e\n\u003cp\u003eAccording to market research by Morningstar\u003csup\u003e26\u003c/sup\u003e, SFDR-labelled article 8 and article 9 funds amounted to 10,608 in December 2022, representing 37.8% of all the funds available for sale in Europe. Our initial sample, of article 8 and 9 funds listed on the Luxembourg Green Exchange, is of 1,389 funds. The assets under management of these funds represent 13.7% of the total AuM of SFDR article 8 and 9 funds (i.e., 630.23\u0026nbsp;billion USD out of 5.01 trillion USD). The total global pool of sustainability-labelled funds, which comprises all types of funds, not only equity funds, is estimated at around 5 trillion, 12 times higher than our sample\u003csup\u003e26\u003c/sup\u003e. Our final sample is reduced to 230 funds, after removing funds with more asset classes (as it leads to double counting for impact intensity metrics) and removing non-equity funds (i.e., funds investing in fixed income or money market funds), as for these we cannot directly apply our proposed assessment model. The sample of equity funds is heterogenous in terms of investment theme and size, ranging from 4\u0026nbsp;million USD to 16.5\u0026nbsp;billion USD in Assets under Management (AuM).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003eSpearman rank correlation\u003c/h2\u003e\n\u003cp\u003eWe apply the Spearman rank correlation, as this is not sensitive to outliers and leads to more reliable results that the traditional, default correlation method used \u0026ndash; the Pearson correlation. The Spearman rank correlation uses the rank of the observations on each variable, thus being described using a monotonic function\u003csup\u003e68\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003eSoftware and data availability\u003c/h2\u003e\n\u003cp\u003eThe data estimation, analysis, and output were performed in python and Jupyter notebooks. The \u003cem\u003epymrio\u003c/em\u003e package\u003csup\u003e69\u003c/sup\u003e was used for input-output calculations, and the \u003cem\u003emySQL workbench\u003c/em\u003e for the financial data retrieval. The environmental impact data is sourced from EXIOBASE database, which is free for academic use, whereas the social impact data is obtained from the PSILCA database, for which a license is needed. Financial data comes from the proprietary database FactSet, and in order to reproduce the code, a licence from the data provider is needed.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBloomberg Intelligence. 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Softw. 9, 1\u0026ndash;11 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3345219/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3345219/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs finance takes on a pivotal role in sustainability transitions, regulations that mandate impact reporting at financial product level are emerging worldwide. Without standardized and reliable indicators, sustainable investing risks being watered down by greenwashing. We show that life-cycle-based, ready-to-use impact indicators can be adapted for sustainability assessment of financial products. We designate 13 environmental and 13 social impact indicators aligned primarily with the EU Sustainable Finance Disclosure Regulation, one of the first pieces of regulation mandating sustainability reporting for financial products. Two major advancements of our framework are the coverage of social impacts and the direct policy relevance. As practical application, we estimate the impacts of a sample of 230 self-labelled sustainable investment funds for all indicators. We show that these investment funds are exposed to significant environmental and social impacts via their investee companies. Total estimated impacts vary between 2.1 and 28.4 times the impacts associated with the consumption of a one million EU citizens, depending on the indicator. Trade-offs could be signalled between and within environmental and social indicators. Finally, most impacts can be traced to a small number of publicly listed companies, which investors could engage with, in order to drive change.\u003c/p\u003e","manuscriptTitle":"Investment funds are responsible for substantial environmental and social impacts with trade-offs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-17 14:37:07","doi":"10.21203/rs.3.rs-3345219/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-earth-and-environment","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsenv","sideBox":"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)","snPcode":"","submissionUrl":"","title":"Communications Earth \u0026 Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"369b711c-1e66-4751-aebf-3f59215e4dda","owner":[],"postedDate":"October 17th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":25286278,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":25286279,"name":"Earth and environmental sciences/Ecology/Environmental economics"}],"tags":[],"updatedAt":"2024-07-02T07:16:12+00:00","versionOfRecord":{"articleIdentity":"rs-3345219","link":"https://doi.org/10.1038/s43247-024-01479-4","journal":{"identity":"communications-earth-and-environment","isVorOnly":false,"title":"Communications Earth \u0026 Environment"},"publishedOn":"2024-06-29 04:00:00","publishedOnDateReadable":"June 29th, 2024"},"versionCreatedAt":"2023-10-17 14:37:07","video":"","vorDoi":"10.1038/s43247-024-01479-4","vorDoiUrl":"https://doi.org/10.1038/s43247-024-01479-4","workflowStages":[]},"version":"v1","identity":"rs-3345219","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3345219","identity":"rs-3345219","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}