Mapping forward-looking mitigation studies at country level

Abstract

We provide the first survey of the rapidly expanding literature on country-level mitigation pathways using systematic mapping techniques. We build a database of 4691 relevant papers from the Web of Science and Scopus. We analyze their abstracts and metadata using text mining and natural language processing techniques. To discover common topics within the abstracts, we use an innovative and fully reproducible topic modeling approach based on two machine learning models. We find that the number of papers per country is well correlated with current levels of greenhouse gas (GHG) emissions, with few papers for (current) low emitters, notably in Africa. Time horizons of 2030 and 2050 each account for one-third of the papers, with the former actually more frequent in recent years, spurred by interest in the (Intended) Nationally Determined Contributions. Topic modeling analysis of the data set reveals that forward-looking mitigation papers encompass all dimensions of mitigation, save for financial issues, that are lacking. However, energy and to a lesser degree land use, land use change and forestry are very dominant relative to other sectors. Topics are unevenly addressed across countries, reflecting national circumstances and priorities, but also pointing to gaps in the literature. The limited number of forward-looking papers in (currently) low-emitting countries raises questions about the lack of research capacity in support of the construction of domestic climate policies.

Keywords

• mitigation pathway
• forward-looking
• national
• topic modeling
• natural language processing