Learning on climate solutions and sustainable development
"Truth is found neither in the thesis nor the antithesis, but in an emergent synthesis which reconciles the two." ― Georg Wilhelm Friedrich Hegel
In a nutshell:
The MCC Working Group on Applied Sustainability Science (APSIS) is interested in understanding climate policy in the wider context of sustainable development. To do so we use a variety of qualitative and quantitative methods from economics, engineering, the social sciences as well as the humanities, but also perform systematic research synthesis to foster learning in science-policy exchanges. Our work is informed by experience at the science-policy interface, novel methods from data science, and in-house expertise on key research topics including fossil fuel market dynamics and the global coal exit, human well-being and sustainable development, climate change mitigation scenarios and negative emissions as well as science-policy exchanges and big data methods for global environmental assessments.
Our unifying theme - research synthesis in times of an exploding literature
The scientific literature on environmental issues such as climate change is rapidly expanding. But the growth in literature is not matched by sufficient synthetic research efforts. Motivated by our experiences in the Intergovernmental Panel on Climate Change (IPCC), APSIS aims at contributing to the development and application of meta-analytical methods and tools that help to transform individual pieces of information into a more coherent map of decision-relevant knowledge, thereby re-aligning the social sciences in climate change research as a systematic, solution-oriented paradigm.
Contemporary efforts to synthesise the scientific literature are confounded by four issues: volume (tens of thousands of existing publications, even in niche fields); velocity (thousands of papers published each month); variety (diverse epistemologies and methodologies); and values (diverse normative assumptions and results).
We attempt to progress on these issues along three avenues of research. First, we apply machine learning and natural language processing methods from the emerging field of big data, which also deals with enormous amounts of information in unstructured forms. These methods help us to derive general as well as specific insights from “big literature”, answering questions such as: What are the scientific discourses taking place on negative emissions technologies? What is the range of costs and potentials associated with a given negative emissions technology?
Second, we are interested more broadly in evidence-based decision making at the science-policy interface. Variety and values in the scientific literature demand a greater attention to the processes of knowledge aggregation: from enhancing the reproducibility of individual studies, to exposing their implicit value-based assumptions, to promoting collaborative processes that will synthesise them into a coherent body of research for identifying promising climate policies and solutions.
Our core topics
1. Coal and Committed Carbon: Coal has been a key driver of economic growth and human development. Yet the release of CO2 and other pollutants induced by its consumption threatens exactly these two goals. This work stream investigates how sustainable development could be achieved by transitioning away from coal for energy purposes. To that end, we are interested in the following research questions: Which factors drive coal consumption? What are the scientific, political and societal discourses on coal transitions and decarbonisation across the world? How have countries managed their past coal transitions? How can we embed the concept of committed carbon into a more realistic socio-economic framework?
2. Human well-being and sustainable development: Well-being concepts such as human needs, quality of life, and the Sustainable Development Goals naturally overlap with the everyday uses of energy and resources within society – the ‘demand-side’ of climate change mitigation. This work stream examines how human well-being is linked to biophysical resource use, and what implications this has for climate policy. For example: Why do some societies require far fewer resources than others to meet their needs? What are the carbon budget implications of extending universal access to basic needs and infrastructures? How do different well-being concepts drive our understanding of central climate issues, such as justice, equity, and appropriate mitigation policy?
3. Climate change mitigation and negative emissions: We evaluate the risks and benefits of different climate change mitigation choices for additional sustainability objectives. Particular attention is given to the role of negative emissions technologies in climate change mitigation that are getting increasingly important to reach international climate goals. For example: How do preferences over non-climate objectives influence the choice of mitigation technologies and timing? Which combination of climate and environmental policies are likely to be effective at which scales to realize synergies and minimize trade-offs? To what extent is there flexibility in the deployment of negative emission technologies for reaching the international climate goals? How can sustainability risks associated with a large-scale deployment of negative emissions technologies be hedged in portfolios?
4. Science policy exchanges and big data methods for global environmental assessments: The exponential growth in scientific literature on environmental issues to levels beyond individual human comprehension is a challenge for global environmental assessments. The availability of large amounts of scientific literature as data, as well as the development of novel methods for interpreting large and networked collections of text, both offer opportunities for helping assessments more systematically engage with large bodies of literature. At the same time, applying these methods to literature, scientific assessments themselves, and the wider public and political discourse, gives us new ways to quantitatively analyse the science-policy interface. We use large networks, natural language processing, and other techniques from the big data world to do applied research on and for the science-policy interface.