Rita Laura D’Ecclesia , Susanna Levantesi , Kevyn Stefanelli
{"title":"Measuring business impacts on the sustainability of European-listed firms","authors":"Rita Laura D’Ecclesia , Susanna Levantesi , Kevyn Stefanelli","doi":"10.1016/j.seps.2024.102078","DOIUrl":null,"url":null,"abstract":"<div><div>The Environmental, Social, and Governance (ESG) themes assume a central position in the foundation of business strategies and risk management for both private managers and financial institutions. Measuring the sustainability commitment of listed companies is required by regulators and Monetary Authorities and plays a pivotal role in the selection process for asset management companies. ESG ratings are used to assess the company’s commitment to sustainability. This paper explores how a firm business, measured by balance sheet data, influences the ESG rating. In particular, we focus on Europe, which countries first paved the way for the sustainable transformation of the economy through various policies and initiatives. We employ a Machine Learning approach to discern the non-linear relationships between ESG ratings and corporate data aiming to identify the prime factors influencing the ESG ratings. We can assess potential country or business sector-based discrepancies by selecting a sample containing firms listed on the major European indices (AEX, BEL, CAC, DAX, FTSE, FTSE-MIB, IBEX, OMX). We find that the firm size, measured by total assets, and the carbon intensity are the variables that most influence the ESG rating in countries where the economic sectors rely mainly on the business cycle and economic conditions. For companies operating in the technology, financials, and industrial sectors, the main ESG driver is the asset turnover ratio, which is a measure of the efficiency with which a company generates revenues, and the EBIT to revenue, which is a measure of the operating margin asset turnover and the Earnings Before Interest and Taxes (EBIT) to revenue ratio. We discover diverse factors affecting ESG ratings across various European countries, highlighting the impact of each nation’s policy on ESG commitment.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"96 ","pages":"Article 102078"},"PeriodicalIF":6.2000,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Socio-economic Planning Sciences","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0038012124002787","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
The Environmental, Social, and Governance (ESG) themes assume a central position in the foundation of business strategies and risk management for both private managers and financial institutions. Measuring the sustainability commitment of listed companies is required by regulators and Monetary Authorities and plays a pivotal role in the selection process for asset management companies. ESG ratings are used to assess the company’s commitment to sustainability. This paper explores how a firm business, measured by balance sheet data, influences the ESG rating. In particular, we focus on Europe, which countries first paved the way for the sustainable transformation of the economy through various policies and initiatives. We employ a Machine Learning approach to discern the non-linear relationships between ESG ratings and corporate data aiming to identify the prime factors influencing the ESG ratings. We can assess potential country or business sector-based discrepancies by selecting a sample containing firms listed on the major European indices (AEX, BEL, CAC, DAX, FTSE, FTSE-MIB, IBEX, OMX). We find that the firm size, measured by total assets, and the carbon intensity are the variables that most influence the ESG rating in countries where the economic sectors rely mainly on the business cycle and economic conditions. For companies operating in the technology, financials, and industrial sectors, the main ESG driver is the asset turnover ratio, which is a measure of the efficiency with which a company generates revenues, and the EBIT to revenue, which is a measure of the operating margin asset turnover and the Earnings Before Interest and Taxes (EBIT) to revenue ratio. We discover diverse factors affecting ESG ratings across various European countries, highlighting the impact of each nation’s policy on ESG commitment.
期刊介绍:
Studies directed toward the more effective utilization of existing resources, e.g. mathematical programming models of health care delivery systems with relevance to more effective program design; systems analysis of fire outbreaks and its relevance to the location of fire stations; statistical analysis of the efficiency of a developing country economy or industry.
Studies relating to the interaction of various segments of society and technology, e.g. the effects of government health policies on the utilization and design of hospital facilities; the relationship between housing density and the demands on public transportation or other service facilities: patterns and implications of urban development and air or water pollution.
Studies devoted to the anticipations of and response to future needs for social, health and other human services, e.g. the relationship between industrial growth and the development of educational resources in affected areas; investigation of future demands for material and child health resources in a developing country; design of effective recycling in an urban setting.