Circular Economy and Beyond: Econometric Modeling of Regenerative Business Practices

The transition from a linear economy to a circular economy represents a critical shift in addressing global sustainability challenges. While circular economy principles—such as waste reduction, resource efficiency, and product lifecycle extension—offer significant promise, integrating these principles into regenerative business practices requires a more nuanced approach. This study explores the econometric modeling of regenerative business practices within the framework of the circular economy, examining how businesses can create systems that not only reduce waste but also restore and regenerate natural and social capital. By leveraging advanced econometric techniques, this research identifies key drivers, evaluates economic and environmental trade-offs, and quantifies the long-term impacts of regenerative strategies. Using a combination of time-series analysis, panel data modeling, and machine learning integration, the study investigates how regenerative practices influence firm performance, stakeholder engagement, and broader economic systems. Case studies across industries are employed to validate the proposed models, offering actionable insights for policymakers and business leaders. The findings underscore the transformative potential of aligning economic incentives with sustainability goals, advancing the discourse on regenerative business practices as a cornerstone for a resilient global economy.

Authors

Publication Details

Journal
Next frontier.
Published
2024-11-07
DOI
https://doi.org/10.62802/0rnzfs83
Primary Topic
Sustainable Supply Chain Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Circular Economy and Beyond: Econometric Modeling of Regenerative Business Practices

Sarp Zülfikaroğlu
Next frontier.
Sustainable Supply Chain Management
article

Circular Economy and Beyond: Econometric Modeling of Regenerative Business Practices

Sarp Zülfikaroğlu
article en

Abstract

The transition from a linear economy to a circular economy represents a critical shift in addressing global sustainability challenges. While circular economy principles—such as waste reduction, resource efficiency, and product lifecycle extension—offer significant promise, integrating these principles into regenerative business practices requires a more nuanced approach. This study explores the econometric modeling of regenerative business practices within the framework of the circular economy, examining how businesses can create systems that not only reduce waste but also restore and regenerate natural and social capital. By leveraging advanced econometric techniques, this research identifies key drivers, evaluates economic and environmental trade-offs, and quantifies the long-term impacts of regenerative strategies. Using a combination of time-series analysis, panel data modeling, and machine learning integration, the study investigates how regenerative practices influence firm performance, stakeholder engagement, and broader economic systems. Case studies across industries are employed to validate the proposed models, offering actionable insights for policymakers and business leaders. The findings underscore the transformative potential of aligning economic incentives with sustainability goals, advancing the discourse on regenerative business practices as a cornerstone for a resilient global economy.

Next frontier.Vol. 8(1)
Decent work and economic growth
Openalex Percentile: Top 66%
Sustainable Supply Chain Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.