George Leopold’s guest post on this topic builds on a paper by Whiteman, Hope and Wadhams, published online yesterday. Their original Nature article is worth reading in its entirety. [It’s already come in for a lot of criticism, from Jason Samenow, for instance; see also Andy Revkin. Most of the criticism is leveled at the natural science, pointing to research that argues a release of methane sufficiently rapid to merit concern is not in the cards.]
Some additional comments prompted by that fuller reading, targeted mostly at the economics.
To begin, the authors emphasize that the $60T cost they estimate (using more refined economic models of the general type used to support the Stern review) is the present discounted value of the aggregated total cost over some 200 years. They compare it with the current world GDP, roughly some $70T per year. That implies, and indeed the authors hint, there’s not just incentive but also time and opportunity to innovate and mitigate. They also conclude that the poorest countries will be hardest hit. As they put it, “The economic consequences will be distributed around the globe, but the modelling shows that about 80% of them will occur in the poorer economies of Africa, Asia and South America. The extra methane magnifies flooding of low-lying areas, extreme heat stress, droughts and storms.” That may indeed be where any cost burden hits initially. However, in multiple ways we’re all in this together, and our destinies are intertwined. Financial sums of any significant size find their way into bills the richer countries will be forced to pay in one form (direct subsidies) or another (relief support for displaced or incapacitated populations; increased national security costs, and more). No one will be immune.
A second comment: this kind of modeling, which integrates both the natural world and human activity in simultaneous calculation, is emerging as an important tool for guiding policy formulation and, through policy, for managing societal decisions and actions. It’s in much the same stage of development as weather forecasting was in the early days. A century ago, the equations governing weather development weren’t so universally known and robustly applied. The model physics was rudimentary at best. Initialization was problematic. Data sources were sparse and the discipline of data assimilation not nearly so mature. When computers finally were put to numerical weather prediction around 1950, they weren’t nearly so capable as today’s. The resulting prognoses inspired little confidence.
But that has changed over the past 70 years. Today’s predictions are more accurate, extending out to greater time horizons, and are the basis not just for emergency response but also decision-making in weather-sensitive economic sectors ranging from agriculture to energy and water resource management.
In the same way, it’s not unreasonable to anticipate that economic projections based on weather and climate scenarios will grow far more useful over coming decades, as natural and social scientists make common cause to couple environmental trends to social outcomes, and gain experience with the task. Exploratory calculations such as those provided by Whiteman et al. should combine with the urgent need to better anticipate future trends to motivate more such research… and such computations. As the critiques above suggest, concatenated or coupled models compound the total resulting uncertainty. A calculation can founder either because of error in the natural science or error in the social piece. As a result, today’s natural-economic models can do little more than stimulate society’s thought process and discussions (as is going on today). But with practice and experience will come skill. We can expect that tomorrow’s models will increasingly guide high-stakes decisions and actions.