As corporate responsibility increasingly incorporates environmental, social, and governance (ESG) criteria, ESG reporting is becoming a legal obligation in many regions. These reports serve as a primary mechanism for organizations to document sustainability practices and for stakeholders to evaluate long-term viability and ethical performance. Ensuring regulatory compliance demands disclosures that are accurate, transparent, and verifiable. However, the complexity and scale of ESG disclosures present challenges for interpretation and automated analysis. To facilitate scalable and trustworthy analysis of these reports, this paper introduces ESG-Bench, a novel benchmark dataset aimed at advancing research in ESG report understanding and hallucination mitigation for large language models (LLMs). ESG-Bench consists of human-annotated question–answer (QA) pairs grounded in real-world ESG report contexts, along with fine-grained labels indicating whether model responses are factually supported or hallucinated. By framing ESG report analysis as a QA task with verifiability constraints, ESG-Bench enables systematic evaluation of LLMs' ability to extract and reason over ESG content. We also uncover a previously unexplored use case: applying ESG-Bench to mitigate hallucinations in socially sensitive and compliance-critical contexts. To this end, we design task-specific Chain-of-Thought (CoT) prompting strategies and fine-tune multiple state-of-the-art LLMs on ESG-Bench using CoT-annotated rationales. Experimental results demonstrate that these CoT-based strategies substantially outperform standard prompting and direct fine-tuning, effectively mitigating hallucinations across benchmarks and highlighting the unique challenges of long-context document reasoning in the ESG setting. We also evaluate our approach across existing QA benchmarks to assess generalization beyond the ESG domain.