Unsupervised Sustainability Report Labeling based on the integration of the GRI and SDG standards (2025)

Abstract: Sustainability reports are key instruments for communicating corporate impact, but their unstructured format and varied content pose challenges for large-scale analysis. This paper presents an unsupervised method to annotate paragraphs from sustainability reports against both the Global Reporting Initiative (GRI) and Sustainable Development Goals (SDG) standards. The approach combines structured metadata from GRI content indexes, official GRI–SDG mappings, and text semantic similarity models to produce weakly supervised annotations at scale. To evaluate the quality of these annotations, we train a multi-label classifier on the automatically labeled data and evaluate it on the trusted OSDG Community Dataset. The results show that our method yields meaningful labels and improves classification performance when combined with human-annotated data. Although preliminary, this work offers a foundation for scalable sustainability analysis and opens future directions toward assessing the credibility and depth of corporate sustainability claims.

## introduction sustainability reporting is increasingly critical as organizations around the world address urgent global challenges such as climate change. clear and standardized disclosures on how companies contribute to sustainability goals help stakeholders assess corporate impacts and inform responsible investments. among several recognized sustainability frameworks, two have emerged as particularly influential: the united nations sustainable development goals (sdgs) (un, 2015), which establish high-level sustainability targets, and the global reporting initiative (gri) 1 , which provides detailed disclosure guidelines for organizations. with sustainability gaining increasing global attention (from climate change to poverty and gender 1 https://www.globalreporting.org/standards/ equality), it is becoming essential to understand how companies report and communicate their actions in these areas. however, sustainability reports are typically extensive, complex, and unstructured documents, making manual annotation and information extraction challenging, costly, and errorprone. despite the significance of climate-focused analysis, the use of nlp to systematically evaluate how companies align their reports with global climate-related sustainability frameworks remains significantly underexplored. for example, consider the following excerpt from pfizer's recent sustainability report: "pfizer was one of the initial signatories to the u.s. department of health and human services (hhs) climate pledge. the pledge, launched in 2022, calls on stakeholders in the u.s. healthcare system (including hospitals, health systems, payers, suppliers, and pharmaceutical companies) to reduce ghg emissions and build a more climate-resilient healthcare infrastructure. by signing, we committed to reduce ghg emissions, publicly report our progress, and develop a climate resiliency plan." this paragraph explicitly addresses climate action and emission reductions. using our proposed method, the paragraph can be automatically linked to specific sdg and gri categories because the text clearly refers to underlying principles and requirements of these frameworks. in particular, it can be annotated as follows: this automated labeling approach bridges the gap between structured sustainability frameworks and unstructured corporate reports, facilitating a large-scale, efficient, and systematic analysis of climate-related disclosures. in this work, we propose an unsupervised annotation pipeline designed to simplify the identification and annotation of paragraphs within lengthy sustainability reports. given the significant costs and limitations associated with manual annotation (including expense, time consumption, and potential interannotator disagreements), our goal is to reduce the manual effort by automatically suggesting highconfidence annotations, which can subsequently be verified by domain experts. we leverage structured human-generated metadata known as gri content indexes, as in @xcite . these indices, typically included in sustainability reports, explicitly link gri standards (covering various environmental, economic, and social impacts) to specific report pages. by focusing on these pages, we narrow the search space, ensuring that we analyze only the potentially relevant sections of otherwise lengthy and complex documents. given these candidate pages, we use established mappings between gri codes and sdgs to further constrain the possible annotations. such mappings significantly reduce ambiguity by limiting the combinatorial explosion that arises from jointly considering 33 gri codes and 17 sdgs. finally, we apply semantic similarity methods based on a pre-trained sentence encoder, such as @xcite or @xcite , to compare each paragraph against textual definitions of the corresponding gri disclosures and sdg targets. this step allows us to rank and select the most plausible annotation pairs (gri, sdg) for each paragraph, resulting in a high-confidence annotated dataset. evaluating the quality of such an unsupervised annotation pipeline directly can be challenging. thus, we propose an indirect evaluation method: training a supervised model on our automatically annotated dataset and testing its performance on an existing benchmark, i.e., the osdg community dataset, presented in @xcite . our hypothesis is that if the addition of our dataset, albeit automatically generated, improves the classification performance, then the generated annotations must contain valuable information foot_0 . our preliminary experimental results show that combining automatically labeled (auto-labeled) data with existing annotated datasets improves the classification accuracy, especially in complex texts, e.g., controversial cases. in the remainder of this paper, section 2 reviews the related literature. section 3 details the annotation methodology. section 4 describes our experimental evaluation and results, and section 5 presents conclusions and future research directions. ## related work previous research relevant to our work can be broadly categorized into three main areas: (1) sustainability reporting frameworks, (2) challenges in annotating sustainability reports, and (3) automated annotation approaches based on nlp. ## bootstrapping gri-sdg annotations via report structure and semantic similarity we propose an unsupervised approach for automatically annotating paragraphs from sustainability reports with relevant gri and sdg labels. our method takes advantage of the structure provided by human-generated gri indexes, known mappings between gri codes and sdgs, and embedding-based semantic similarity. ## experimental evaluation in this section, we evaluate the effectiveness of the weakly supervised dataset produced by our annotation pipeline. since direct validation of unsupervised labels is challenging, we adopt an indirect strategy: we train a classifier on our automatically labeled data and test it on a manually annotated benchmark. the underlying assumption is that if our annotations capture meaningful sdg-related semantics, they should improve performance on the downstream classification task. experimental setup. the proposed methodology was applied to a dataset consisting of 30 sustainability reports published in 2023. these reports span 10 industry sectors (energy, health care, mining, food and beverages, chemicals, high-tech, land and soil, manufacturing, services, and textile) with three reports selected per sector. in total, the corpus includes 3,663 pages. after preprocessing and paragraph segmentation (see step 1 in section 3), we extracted 19,133 paragraphs. of these, 10,303 paragraphs fall within the page ranges indicated by gri content indexes (step 2), and are retained for candidate annotation. for sdg enrichment, we use the official gri-sdg mapping from the action platform reporting on sdgs foot_4 . the mapping includes all 33 gri codes and all 17 sdgs, resulting in 89 distinct (g, s) ∈ g × s pairs used to guide the annotation process. we use the all-mpnet-base-v2 pre-trained model from the sentence-transformers library foot_5 to compute paragraph and label embeddings. this model, based on mpnet @xcite , has been fine-tuned for sentence-level tasks such as semantic similarity and sentence matching. it combines strengths from both bert @xcite and xlnet @xcite , and has demonstrated strong performance in sustainabilityrelated annotation tasks @xcite . we obtain a total of 10, 303 annotated paragraphs, each labeled with one or more (gri, sdg) pairs. the distribution of labels in the 17 sdgs is markedly uneven. sdg 2 -hunger (about achieve food security and sustainable agriculture) has the fewest annotations (128), while sdg 8 -economy (about promote sustainable economic growth and decent work) appears most frequently (3,857). on average, each sdg is associated with 894 annotated paragraphs, with a standard deviation of 976, reflecting significant variability in the frequency of the label. to evaluate the quality of our automatically labeled data, we rely on the open source sdg (osdg) community dataset 8 . in this dataset, each paragraph is associated with a single sdg and validated through binary judgments (agree/disagree) by multiple annotators. while this one-label-perparagraph setting is a simplification (since many texts plausibly relate to multiple sdgs), it reflects the task's inherent ambiguity. each example is also accompanied by an agreement score that indicates how consistent the annotators were in accepting the proposed sdg. we use this agreement score to build two sets of evaluations of increasing complexity: lower-agreement examples (below 0.7) are excluded from the test due to their inherent ambiguity and noise. for training, we consider four configurations to assess the utility of our auto-labeled data and its interaction with osdg: for the evaluation, we fine-tuned a custom bert-based classifier (bert-base-cased) for multi-label classification 9 as such encoder based classifiers provide a strong baseline and have shown robust performance on sdg, gri, and esg label prediction in prior work @xcite 8 https://github.com/osdg-ai/osdg-data 9 training was performed using an effective batch size of 16 (with gradient accumulation every 4 mini-batches of size 4), a learning rate of 2 × 10 -5 , weight decay of 0.1, and a warmup ratio of 0.1. the model was trained for 5 epochs using the adamw optimizer and a linear learning rate scheduler. @xcite . a key challenge arises from the difference in annotation schemes: the osdg dataset provides only a single sdg code per paragraph, whereas our autolabeled dataset may associate a paragraph with multiple sdg codes, an approach we argue to be more realistic. to accommodate this, the model was trained in a multi-label setting, allowing it to output potentially more than one sdg per instance. however, during the evaluation, only the label with the highest predicted probability was selected to remain consistent with the single-label format of the osdg test set. model performance was reported in terms of accuracy, calculated as the percentage of paragraphs that were correctly reassigned to their original sdg label. ## conclusion and future work this work introduces an unsupervised pipeline for annotating sustainability report paragraphs with both gri and sdg labels. by combining structured metadata from gri content indexes, official sdg-gri mappings, and semantic similarity scoring via sentence embeddings, we create a weakly supervised dataset that can be used to support downstream classification tasks. a preliminary evaluation using the osdg benchmark demonstrates that our auto-labeled data (despite being generated without human supervision) contributes positively when combined with high-quality manually annotated datasets. these early results suggest that our annotations capture meaningful sustainability semantics and can complement existing resources. this paper represents the first step in a wider ongoing effort. although our current evaluation focuses on sdg labels and uses an indirect performance-based validation, further work is needed to better assess the quality and reliability of the sdg annotations-ideally through targeted human evaluations and to benchmark our fine-tuned classifier, trained on automatically labeled data, against established methods in the literature @xcite @xcite . in addition, our method currently generates gri labels, but these have not yet been empirically validated; assessing their accuracy is a priority for future iterations. more importantly, our long-term goal extends beyond surface-level classification. we aim to deepen the analysis of sustainability discourse within reports by examining the nature of reported content (distinguishing between factbased, temporally grounded disclosures and vague, qualitative claims). this is particularly relevant in detecting subtle forms of greenwashing. future work will focus on moving beyond topic classification to assess the substance and credibility of reported content, to identify meaningful disclosures as opposed to vague or unsubstantiated claims, and extend the scope to include esg reporting.