Advancing Sustainable Solutions

Businesses are an essential driver for sustainability, yet managers struggle to make decisions about their business model that actually improve their sustainability performance. In this episode, we meet Florian Lüdeke-Freund, Henning Breuer, and Lorenzo Massa, authors of the new book called ‘Sustainable Business Model Design’. They introduce the concept of sustainable business model patterns, to support practitioners eager to make sustainability work in their business context. A sustainable business model pattern is knowledge based on experience, which includes a statement of a challenge, and a suggestion for a solution to address this challenge, with the deliberate aim of creating sustainable value. Thus, patterns represent templates to be adapted from one context to another to support creativity and experimentation. We provide several examples of patterns, as well as ways that practitioners may use patterns in their context. This episode is the final episode of our fourth season – we will be back in September 2022 with a new episode every month! Until then!

There is a clear and urgent need to accelerate the implementation of sustainability solutions. We must move beyond simple conceptual understanding of solutions, instead experimenting with collaborators in contexts in order to improve effectiveness, share learnings with others, and avoid future mistakes that waste time. In this episode, we discuss nature-based solutions as a holistic approach addressing the why, what, who, and how to achieve sustainability through integrating nature in cities. PhD Student Björn Wickenberg shares his latest research on implementing nature-based solutions, including his three strategies for urban planners. Finally, we discuss skills – collaboration, experimentation, evaluation, and learning – in order to support implementation in the right configurations in the right context, as well as to minimize any rebound effects.

Today, more than half of the world’s population live in cities, and by mid-century it is predicted that more than two-thirds of the world’s population will live in urban areas. This sees cities at the forefront of sustainability, needing to meet social needs within our ecological boundaries. An overarching guiding roadmap to consider sustainability is the United Nations’ Sustainable Development Goals - the SDGs. These 17 goals cover all aspects of sustainable development, and cities have an important role to play as more than half of the targets will need engagement from local and regional authorities in order to be achieved. How then are these international goals integrated into local city planning and development? In today’s episode we address this question, and explore how academia and cities may support each other in this endeavour. We talk to Roland Zinkernagel, a municipal PhD student who shares his unique experiences from working with sustainable urban planning in the Swedish city of Malmö, while also researching how the SDGs can be applied in a local city context.

Artificial intelligence - AI - is being applied throughout society to a wide array of sustainability-related challenges, where it can help to effectivize and optimize systems to save both energy and resources. AI can also be used to analyse enormous amounts of data, faster than any human ever could. However, AI is very energy demanding, and comes with ethical concerns as it may require sensitive data, as it may reinforce biases and prejudices, and as is it may also be used to mislead or misinform citizens. So, let’s ask ourselves the question: what is the role of artificial intelligence in helping to advance sustainable solutions? To answer this question, we must explore the potential of artificial intelligence as well as any trade-offs associated with energy consumption, ownership of data and code, as well as privacy and other ethical concerns. In this episode we are joined by Sonja Aits, Associate Senior Lecturer at the Department of Experimental Medical Science, and Sergio Rico, a PhD researcher at the Computer Science Department at Lund University, to help us further explore the opportunities and responsibilities that come with using AI.

Every year, the world adds more renewable energy production capacity than the year before. But, looking historically, is this growth enough to achieve our climate targets? According to a recent publication in ‘Nature Energy’, the short answer is “no” – to meet the climate goals requires decades of growth in renewables at rates higher than those observed historically in most countries. In this episode, we chat with two of the co-authors – Jessica Jewell and Aleh Cherp – about their research, which examined historical data among 60 countries, and modeled an average maximum growth rate of wind and solar of approximately 1% per year. However, this growth rate has not been sustained over time in any country at the levels needed to meet many of the climate mitigation scenarios. To understand the implications of their research, we learn about the technology adoption lifecycle and the technology diffusion process. Finally, we discuss why technological learning may not speed up future growth, as countries lagging behind adoption of renewable energy may have less favorable conditions compared to early adopters. Learn more about their research at https://www.polet.network/.