Development of an integrated ESG and climate risk assessment framework for semiconductors industries in Malaysia

• Environmental, Social, Governance valuation of semiconductors plants versus targets. • Following TCFD recommendations in semiconductors industry sustainability reports. • Use Proximity to Target approach to measure sustainability performance indices. The semiconductor industry is an important sector of today’s world economy. Given its explosive growth, it is crucial for this industry to adopt an Environmental, Social, and Governance (ESG) approach in line with the United Nations Sustainable Development Goals (UNSDG). Despite the recent movement of manufacturers towards establishing their ESG data collection, a quantitative tool to compare their performance against standards is limited. Noticing its significant contribution to climate change, semiconductor manufacturers also need to follow Task Force on Climate-related Financial Disclosures (TCFD) recommendations in their sustainability reports. In Malaysia’s case, all public listed companies must perform this disclosure by 2025 according to the Bursa Malaysia announcement, but this is still lacking. Thus, this study aims to develop a comprehensive framework for ESG assessment of the semiconductor industry in Malaysia, following the TCFD recommendations in climate risk regards. A framework comprising eight steps employing the proximity-to-target (PTT) approach is used to measure indices performance level of two case studies located in Malaysia: Murata Manufacturing Co., Ltd. and ViTrox Corporation Bhd. A zero-to-one rating is developed for certain vital indicators to be quantitatively reflected in the assessment. From the results, Murata topped ViTrox overall ESG index score. This result is attributed to the parameter weightage concluded as the core influencer in the assessment. As a recommendation, it is advised to conduct a more precise materiality assessment that incorporates relevant stakeholders’ input, considers the sovereign ESG factors in metrics, and incorporates machine learning to improve the framework's adaptability to other industries.