In the last 50 years, the biosphere, upon which humanity depends, has been altered to an unparalleled degree. The current economic model relying on fossil resources and addicted to “growth at all costs” is putting at risk not only life on our planet, but also the world’s economy. The need to react to the unprecedented COVID-19 crisis is a unique opportunity to transition towards a sustainable wellbeing economy centered around people and nature. After all, deforestation, biodiversity loss and landscape fragmentation have been identified as key processes enabling direct transmission of zoonotic infectious diseases. Likewise, a changing climate has profound implications for human health. Putting forward a new economic model requires transformative policies, purposeful innovation, access to finance, risk-taking capacity as well as new and sustainable business models and markets. But above all we need to address the past failure of our economy to value nature, because our health and wellbeing fundamentally depends on it. A circular bioeconomy offers a conceptual framework for using renewable natural capital to holistically transform and manage our land, food, health and industrial systems with the goal of achieving sustainable wellbeing in harmony with nature. Within the framework of the Sustainable Markets Initiative, under the leadership of His Royal Highness The Prince of Wales, a 10-Point Action Plan to create a circular bioeconomy is proposed below. The Action Plan is a response to The Prince of Wales’ call to invest in nature as the true engine for our economy. The Action Plan, guided by new scientific insights and breakthrough technologies, is articulated around six transformative action points further discussed below and four enabling action points, which mutually reinforce each other.
An age-structured bioeconomic model, which is completely continuous in age and time, is developed in order to compare with traditional discrete models. Both types have advantages and disadvantages. The continuous framework complements discrete models as it allows for deeper and more transparent analytical study and leads to analytical results that would be difficult to achieve within a discrete framework. To make the model realistic, a nonlinear recruitment function is introduced and steady state solutions and constant-effort optimal fishing are studied analytically. In addition, the framework has been used for numerical analysis. Simulations are used to investigate how optimal harvesting patterns vary with parameter values.