Innovations in process optimization for environmental sustainability in emerging markets
Year: 2024
Type: article
Abstract: This review explores innovations in process optimization for environmental sustainability in emerging markets. It aims to examine the integration of advanced technologies, economic and social impacts, and policy frameworks that facilitate the transition toward sustainable industrial practices. Utilizing a comprehensive literature review methodology, the study identifies key trends, including the adoption of digital technologies such as artificial intelligence (AI), Internet of Things (IoT), and renewable energy sources, which are central to optimizing processes and reducing environmental footprints. The main findings reveal that process optimization not only improves operational efficiency but also fosters social equity by creating jobs and improving the livelihoods of local communities. Case studies from India, China, and Brazil demonstrate how industries have successfully implemented optimization strategies that have resulted in significant cost reductions, reduced resource consumption, and lower carbon emissions. However, challenges such as regulatory inconsistencies, limited access to financing, and technological barriers remain significant obstacles to widespread adoption. The study concludes that while technological innovation is critical, supportive policy frameworks are equally important to encourage industries to adopt sustainable practices. Recommendations include strengthening policy incentives, improving regulatory enforcement, and increasing access to green financing. By addressing these challenges, emerging markets can enhance their competitiveness in a global economy increasingly focused on sustainability. The study offers valuable insights for policymakers, industries, and researchers looking to drive sustainable industrial practices in emerging economies.
Language: English
Subfield: Management of Technology and Innovation
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4403494522