Dataset Information

What's stopping the waste-treatment industry from adopting emerging circular technologies? An agent-based model revealing drivers and barriers

ABSTRACT: Highlights • Adoption feasibility remains a central issue in the Circular Economy.• Agent-based modeling is proposed to connect internal and ecosystem-level factors.• Analyses show the role of efficiencies, market growth, support frameworks and social pressures.• The adoption timing can be determined by the speculated market growth and technology improvements.• Supporting slow-growing technologies could result in sector-wide circular economy adoption. Many new circular economy technologies are gaining momentum, yet research on the complexity of adoption decisions driven by uncertainties, both at technology and ecosystem level, is lacking. In the present study, an agent-based model was developed to study factors that influence the adoption of emerging circular technologies. The case of the waste treatment industry was chosen, specifically its (non-) adoption of the so-called “Volatile Fatty Acid Platform”, a circular economy technology that facilitates both the valorization of organic waste into high-end products as well as their sale on global markets. Model results show adoption rates under 60% due to effects of subsidies, market growth, technological uncertainty and social pressure. Furthermore, the conditions were revealed under which certain parameters have the most effect. An agent-based model enabled use of a systemic approach to reveal the mechanisms of circular emerging technology innovation that are most relevant for researchers and waste treatment stakeholders.

SUBMITTER: Farahbakhsh S

PROVIDER: S-EPMC9936780 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Rapid population growth has increased the global demand for food. However, some studies have revealed that more than one-third of the global food production is lost during postharvest operations along the food supply chain. Managerial and technical limitations such as a lack of proper storage facilities, poor handling practices and, associated knowledge are among the main causes of food losses particularly in Africa. Maize is among the most important staple food and cash crops across sub-Saharan Africa. In Rwanda, most of the maize produced incurs losses during storage. Hermetic storage technologies (HST) have been proven to be effective in the control of post-harvest storage losses in maize. However, their adoption in maize storage has been low with farmers continuing to use less effective technologies. Therefore, this study assessed the impact of hermetic maize storage technologies adoption, on the income of 301 randomly selected smallholder maize farmers in Gatsibo District of Rwanda, using the endogenous switching regression (ESR). The results revealed that household size, post-harvest training, access to credit, distance to input provider, and the household head's experience in maize production were the major factors influencing farmers' decision to adopt HST. The occupation of the household head, number of plots reserved for other crops, training, household size, age of the household head, and household maize self-sufficiency goal significantly influenced income for both HST adopters and non-adopters. Overall, the adoption of HST had a positive and significant impact on income from stored maize, among those who adopted it. The study recommends that the government of Rwanda and other stakeholders should support the dissemination of HST to facilitate access. Thus, increased access to institutional support services such as post-harvest training, credit access, and agricultural input supply, should be a major part of efforts aimed at promoting the effective use of hermetic maize storage technologies among smallholder maize farmers in Rwanda.

Dataset Information

What's stopping the waste-treatment industry from adopting emerging circular technologies? An agent-based model revealing drivers and barriers

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets