High-quality agricultural carbon credits that incentivize regenerative practices can help address climate change through greenhouse gas (GHG) abatement and CO2 sequestration. Generating large volumes of such credits requires rigorous crediting methodologies. The Soil Enrichment Protocol (SEP) by the Climate Action Reserve (CAR) aims to unlock this type of crediting potential. The SEP includes new expert-driven standards for validating the use of soil biogeochemical modeling to generate credits. Technical experts at Indigo Ag participated in the SEP working group and are supporting implementation of the first project, CAR 1459_RP1, on hundreds of thousands of acres in the US. The authors share their thoughts on new approaches enabled by the SEP as both contributors to the theory behind and practitioners of these approaches. The SEP enables scalable, high-quality credits through four main advances: (1) allowing flexibility in the use of biogeochemical models that meet explicit performance requirements, (2) enabling a new approach to field-level, modeled baselines, (3) supporting a hybrid approach of credit generation using both soil measurement and modeling, and (4) requiring a new type of credit uncertainty quantification that accounts for multiple sources of uncertainty. Together these advances support agricultural credit quantification that enables payments to offset transitional costs for growers, at large enough scales to create a robust market, with a level of rigor that ensures any credited emission reductions have real climate impact. Innovations in soil analyses, advances in research, and improvements in data collection could further improve the potential for agricultural carbon credits to scale.

We combine a sequence of machine-learning techniques, together called Principal Smooth-Dynamics Analysis (PriSDA), to identify patterns in the dynamics of complex systems. Here, we deploy this method on the task of automating the development of new theory of economic growth. Traditionally, economic growth is modeled with a few aggregate quantities derived from simplified theoretical models. PriSDA, by contrast, identifies important quantities. Applied to 55 years of data on countries’ exports, PriSDA finds that what most distinguishes countries’ export baskets is their diversity, with extra weight assigned to more sophisticated products. The weights are consistent with previous measures of product complexity. The second dimension of variation is proficiency in machinery relative to agriculture. PriSDA then infers the dynamics of these two quantities and of per-capita income. The inferred model predicts that diversification drives growth in income, that diversified middle-income countries will grow the fastest, and that countries will converge onto intermediate levels of income and specialization. PriSDA is generalizable and may illuminate dynamics of elusive quantities such as diversity and complexity in other natural and social systems.

Poor economies not only produce less; they typically produce things that involve fewer inputs and fewer intermediate steps. Yet the supply chains of poor countries face more frequent disruptions—delivery failures, faulty parts, delays, power outages, theft and government failures—that systematically thwart the production process. To understand how these disruptions affect economic development, we modelled an evolving input–output network in which disruptions spread contagiously among optimizing agents. The key finding was that a poverty trap can emerge: agents adapt to frequent disruptions by producing simpler, less valuable goods, yet disruptions persist. Growing out of poverty requires that agents invest in buffers to disruptions. These buffers rise and then fall as the economy produces more complex goods, a prediction consistent with global patterns of input inventories. Large jumps in economic complexity can backfire. This result suggests why ‘big push’ policies can fail and it underscores the importance of reliability and gradual increases in technological complexity.