| Abstrakt: |
Logistics industry has become critical for each country to fully reap benefits of globalization through product, process and technology, cross-border movement of goods and services. Trade among countries, both in goods and services, has increased during the past five decades. Globalization was based on the two pillars of free trade and opening up economies for human, capital, and technology migration with minimal or no obstacles. The very fact that the integrated supply chain, which is the backbone of globalization, got disrupted with the onset of the Covid-19 pandemic. Unexpected lockdowns and stringent travel restrictions led to a fall in production and a lag in the supply chain to meet the delivery expectations of the customers. Secondly, fast economic growth with growing competition led to huge challenges to the environment, carbon emissions, climate change, as well as social and governance risks. Efforts by the relevant global institutions to focus on Sustainable Development Goals have become questionable with increased challenges to the Environment, Social, and Governance (ESG) risks faced by companies, industries, and countries. It is in this context; the present research study attempts to study the relationship between financial performance and ESG compliance empirically. The logistics industry comprises transport, shipping, warehouses, and freight forwarding activities that lead to CO2 emissions, air and water pollution, and increased use of fossil fuels, which pose a reasonable challenge to the Sustainable Development Goals and commitments made by each country to reduce the carbon footprint. However, the logistics sector companies, both global and domestic, are trying to reduce the carbon footprint with the help of innovation, technology, and sustainable development approaches. Our research question is to test whether the companies that are conscious and spending on ESG compliance have been able to achieve higher financial performance or not. Select literature review suggests mixed results leading to research gaps. Panel data models are specified and estimated. Results, conclusions, and managerial implications are discussed. [ABSTRACT FROM AUTHOR] |
Full text from SpringerLink