Abstract: Sustainability challenges related to food production arise from multiple nature-society interactions occurring over long time periods. Traditional methods of quantitative analysis do not represent long-term changes in the networks of system components, including institutions and knowledge that affect system behavior.

Here, we develop an approach to study system structure and evolution by combining a qualitative framework that represents sustainability-relevant human, technological and environmental components (HTE), and their interactions, mediated by knowledge and institutions, with network modeling that enables quantitative metrics. We use this approach to examine the water and food system in the Punjab province of the Indus River Basin in Pakistan, exploring how food production has been sustained, despite high population growth, periodic floods, and frequent political and economic disruptions. Using network models of five periods spanning seventy-five years (1947-2022), we examine how quantitative metrics of network structure relate to observed sustainability-relevant outcomes and how potential interventions in the system affect these quantitative metrics.

We find that the persistent centrality of some and evolving centrality of other key nodes, coupled with the increasing number and length of pathways connecting them are associated with sustaining food production in the system over time. Assessment of potential interventions regulating groundwater pumping and phasing out fossil-fuels could alter network pathways and identify potential vulnerabilities for future food production.

Significance Statement: Models for informing sustainability interventions in complex adaptive systems involving nature-society interactions are challenging to construct due to lack of detailed, quantitative data on the changing structure of system interactions. Here, we develop a new approach combining qualitative descriptions of system components and interactions, with network representation for quantitative characterization of system structure. We demonstrate this approach with retrospective and prospective analyses related to food production in Pakistan’s Indus River Basin. Results identify the nodes and increasing number of pathways associated with sustained food production. Future scenarios point to production vulnerability due to conversion of arable land with implications on livelihoods for laborers and small business owners and highlight the importance of coordinating rural and urban water and land-use policies.

Abstract: Sulfur hexafluoride (SF₆) is a potent greenhouse gas. Here we use long-term atmospheric observations to determine SF₆ emissions from China between 2011 and 2021, which are used to evaluate the Chinese national SF₆ emission inventory and to better understand the global SF₆ budget. SF₆ emissions in China substantially increased from 2.6 (2.3-2.7, 68% uncertainty) Gg yr⁻¹ in 2011 to 5.1 (4.8-5.4) Gg yr⁻¹ in 2021.

The increase from China is larger than the global total emissions rise, implying that it has offset falling emissions from other countries. Emissions in the less-populated western regions of China, which have potentially not been well quantified in previous measurement-based estimates, contribute significantly to the national SF₆ emissions, likely due to substantial power generation and transmission in that area. The CO₂-eq emissions of SF₆ in China in 2021 were 125 (117-132) million tonnes (Mt), comparable to the national total CO₂ emissions of several countries such as the Netherlands or Nigeria.

The increasing SF₆ emissions offset some of the CO₂ reductions achieved through transitioning to renewable energy in the power industry, and might hinder progress towards achieving China’s goal of carbon neutrality by 2060 if no concrete control measures are implemented.

Abstract: Methane (CH₄) is the second most critical greenhouse gas after carbon dioxide, contributing to 16-25% of the observed atmospheric warming. Wetlands are the primary natural source of methane emissions globally. However, wetland methane emission estimates from biogeochemistry models contain considerable uncertainty. One of the main sources of this uncertainty arises from the numerous uncertain model parameters within various physical, biological, and chemical processes that influence methane production, oxidation, and transport. Sensitivity Analysis (SA) can help identify critical parameters for methane emission and achieve reduced biases and uncertainties in future projections. This study performs SA for 19 selected parameters responsible for critical biogeochemical processes in the methane module of the Energy Exascale Earth System Model (E3SM) land model (ELM). The impact of these parameters on various CH₄ fluxes is examined at 14 FLUXNET- CH₄ sites with diverse vegetation types. Given the extensive number of model simulations needed for global variance-based SA, we employ a machine learning (ML) algorithm to emulate the complex behavior of ELM methane biogeochemistry. ML enables the computational time to be shortened significantly from 6 CPU hours to 0.72 milliseconds, achieving reduced computational costs. We found that parameters linked to CH₄ production and diffusion generally present the highest sensitivities despite apparent seasonal variation. Comparing simulated emissions from perturbed parameter sets against FLUXNET-CH₄ observations revealed that better performances can be achieved at each site compared to the default parameter values. This presents a scope for further improving simulated emissions using parameter calibration with advanced optimization techniques like Bayesian optimization.

Abstract: Future configurations of the power system in the central region of the U.S. are dependent on relative costs of alternative power generation technologies, energy and environmental policies, and multiple climate-induced stresses. Higher demand in the summer months combined with compounding supply shocks in several power generation technologies can potentially cause a “perfect storm” leading to failure of the power system. Potential future climate stress must be incorporated in investment
decisions and energy system planning and operation.

We assess how projected future climate impacts on the power system would affect alternative pathways for the electricity sector considering a broad range of generation technologies and changes in demand. We calculate a “potential supply gap” metric for each pathway, system component, and sub-region of the U.S. Heartland due to climate-induced effects on electricity demand and power generation. Potential supply gaps range from 5% in the North Central region under mild changes in climate to 21% in the Lakes-Mid Atlantic region under more severe climate change.

We find increases in electricity demand to be more important in determining the size of the potential supply gap than stresses on power generation, while larger shares of renewables in the power system contribute to lower supply gaps. Our results provide a first step toward considering systemic climate impacts that may require changes in managing the grid or on potential additional  capacity/reserves that may be needed.