# A comparative scoping review approach: identifying the intersection of carbon, biodiversity, and water offsetting

## TL;DR

This paper introduces a new method to compare carbon, biodiversity, and water offsetting research, highlighting their interconnections and offering a practical approach for managing large volumes of literature.

## Contribution

The paper presents an adapted scoping review methodology that unifies and compares three distinct offsetting literatures using a consistent and scalable approach.

## Key findings

- The method enables comparability across carbon, biodiversity, and water offsetting literature.
- ASReviewer and pilot screening improve efficiency and consistency in literature screening.
- Combining bibliometric and thematic analysis provides a holistic view of interrelationships among offsetting areas.

## Abstract

Environmental and climate policies, as well as the knowledge underpinning them, are often developed in isolation. This is evident in offsetting research and policy, which tend to address carbon, biodiversity, and water as separate issues. This paper presents the development of an adapted scoping review methodology to compare these three distinct bodies of literature within a unified framework, which also allows for the introduction of the emerging water offsetting literature. The approach ensures comparability across datasets of relevant literature while addressing the challenge of managing large volumes of literature within time and resource constraints. It provides a practical solution for managing diverse bodies of literature in scoping reviews, enabling a holistic understanding of the interrelationships among carbon, biodiversity, and water offsetting.

Key elements of the method include:•Applying a consistent approach across all three datasets, while accommodating the specificities of each.•Utilizing the machine learning tool ASReviewer to streamline the screening process, alongside a pilot screening phase to establish consistent inclusion criteria.•Combining quantitative bibliometric analysis with qualitative thematic analysis.

Applying a consistent approach across all three datasets, while accommodating the specificities of each.

Utilizing the machine learning tool ASReviewer to streamline the screening process, alongside a pilot screening phase to establish consistent inclusion criteria.

Combining quantitative bibliometric analysis with qualitative thematic analysis.

Image, graphical abstract