  Sustainability commonly refers to entities, such as individuals, companies,
and institutions, having a non-detrimental (or even positive) impact on the
environment, society, and the economy. With sustainability becoming a synonym
of acceptable and legitimate behaviour, it is being increasingly demanded and
regulated. Several frameworks and standards have been proposed to measure the
sustainability impact of corporations, including United Nations' sustainable
development goals and the recently introduced global sustainability reporting
framework, amongst others. However, the concept of corporate sustainability is
complex due to the diverse and intricate nature of firm operations (i.e.
geography, size, business activities, interlinks with other stakeholders). As a
result, corporate sustainability assessments are plagued by subjectivity both
within data that reflect corporate sustainability efforts (i.e. corporate
sustainability disclosures) and the analysts evaluating them. This subjectivity
can be distilled into distinct challenges, such as incompleteness, ambiguity,
unreliability and sophistication on the data dimension, as well as limited
resources and potential bias on the analyst dimension. Put together,
subjectivity hinders effective cost attribution to entities non-compliant with
prevailing sustainability expectations, potentially rendering sustainability
efforts and its associated regulations futile. To this end, we argue that
Explainable Natural Language Processing (XNLP) can significantly enhance
corporate sustainability analysis. Specifically, linguistic understanding
algorithms (lexical, semantic, syntactic), integrated with XAI capabilities
(interpretability, explainability, faithfulness), can bridge gaps in analyst
resources and mitigate subjectivity problems within data.
