Do we need more rigor to solve climate change, improve food security, and increase biodiversity on marginal, degraded, barren and underserved lands? Certainly, not in the way rigor was initially defined as “harsh inflexibility in opinion, temper, or judgment” or “the quality of being unyielding or inflexible.” Yet, this approach is too often taken as we evaluate new and innovative approaches to climate solutions. This stance can prematurely shut down or stifle the best creative ideas and their implementation. Rigor mortis, if you will.
So no, we don’t want more rigor in the traditional sense. Quite the opposite if science, technology, and the human condition are to advance. An open-minded, inquisitive, and incisive consideration of potential cutting-edge solutions is preferable if we are to make optimal and timely progress on the challenge of climate change.
Qualifiers help with this double-edged sword of rigor.
What we need is “scientific rigor,” which becomes more vital with every passing day. Scientific rigor is “the strict application of the scientific method to ensure unbiased and well-controlled experimental design, methodology, analysis, interpretation and reporting of results.” That comes closer to our intent. Perhaps we should add mathematical rigor - the quest for exactitude and focus on accuracy. And statistical rigor - the quantification and understanding of uncertainty. And without question, intellectual rigor, the ability to make well-reasoned arguments supported by logic, research and new and existing data is important for introducing, implementing and scaling new and innovative ideas.
How can scientific, mathematical, statistical and intellectual rigor contribute to the best nature-based solutions - delivering results that make a difference? We can begin by encouraging innovation, asking the right questions and quantitatively testing a thoughtfully constructed hypothesis. Baseline studies form the foundation for evaluating nature-based solutions, particularly carbon additionality. By creating a clear understanding of the current state of carbon stock and fluxes in a given area, soil health and biodiversity good baseline studies enable the establishment of the reference upon which future carbon sequestration efforts can be measured. Such studies involve collecting comprehensive in-situ and proxy data and considering historical trends and past and projected land-use changes.
Scientifically rigorous baseline studies and follow-up sampling and measurement should provide results that are ultimately statistically significant to ensure an accurate and reliable assessment of carbon additionality. Statistical significance allows for the detection of meaningful differences between projects under development and controls, enabling the determination of whether carbon additionality projects are leading to additional carbon sequestration in soil. Robust statistical analyses incorporating the appropriate statistical tests help avoid spurious correlations and enhance confidence in the observed results. Standard statistical tests may only be appropriate where data are normally distributed, and non-parametric methods may be considered. See Stanley, 2023; Spertus, 2021, for some recent examples.
Disciplined stratification, avoidance of “hotspots,” sufficient sampling numbers and depth, data-driven compositing strategies, and consideration of the best statistical methods add to confidence in results and reductions in uncertainty.
In most things, it’s essential not only to be ahead of the curve but to stay ahead. Scientific rigor, mathematical rigor, statistical rigor and logical rigor independently and combined, and minds open to what is new and different keep us well positioned in a constantly evolving landscape of standards, methodologies, project validation and verification, offsets, credits, carbon markets, policy and regulation. Rigor, not so much.
More good reads:
National Institute of Health, 2023, Enhancing Reproducibility through rigor and transparency
Stanley et al., 2023. Valid inferences about soil carbon in heterogeneous landscapes
Spertus, 2021; Optimal Sampling and Assay for Estimating Soil Organic Carbon
