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At least 145 records · Page 8

The impact of climate change on Korea’s agricultural sector under the national self-sufficiency policy

Evolving environmental conditions due to climate change have brought about changes in agriculture, which is required for human life as both a source of food and income. International trade can act as a buffer against potential negative impacts of climate change on crop yields, but recent years have seen breakdowns in global trade, including export bans to improve domestic food security. For countries that rely heavily on imported food, governments may institute policies to protect their agricultural industry from changes in climate-induced crop yield changes and other countries’ potential trade restrictions. This study assesses the individual and combined effects of climate impacts and food self-sufficiency policies in Korea, which is highly dependent on imports. We use the Global Change Analysis Model (GCAM), a global integrated assessment model, to explore (1) the direct impact of climate change on Korea’s agricultural yields, (2) the full impacts of global climate change on agricultural production, including trade-induced changes due to yield changes in other regions, (3) the impacts of food self-sufficiency policy, and (4) the interactive impact of climate change and self-sufficiency policies. We find that, in Korea, the direct impact of climate change on agricultural yields would be overshadowed by the impact of global climate change due to changing trade patterns. Second, global climate change leads to a rise (rice and wheat) or a decline (soybeans) in Korean producer revenues, while simultaneously raising consumer expenditures on both staples and non-staples. Third, implementing self-sufficiency policies for wheat and soybeans in Korea boosts the nation’s producer revenues, in conjunction with the effects of climate change, at the cost of additional increases in consumer expenditures for both staples and non-staples.

Science & Technology - Other Topics↗

ambrosia: An R package for calculating and analyzing food demand that is responsive to changing incomes and prices

The ambrosia R package was developed to calculate food demand for staples and non-staple commodities that is responsive to changing levels of incomes and prices. ambrosia implements the framework to quantify food demand as established by Edmonds et al. (Edmonds et al. 2017) and allows the user to explore and estimate different variables related to the food demand system. Currently ambrosia provides 3 main functions: (1) calculation of food demand for any given set of parameters including income levels and prices (2) estimation of parameters within a given a dataset. Note: ambrosia is used to calculate parameters for the food demand model implemented in the Global Change Analysis Model(GCAM) (3) exploration and preparation of raw data before starting a parameter estimation

97 MATHEMATICS AND COMPUTING↗

rmap: An R package to plot and compare tabular data on customizable maps across scenarios and time

`rmap` is an R package that allows users to easily plot tabular data (CSV or R data frames) on maps without any Geographic Information Systems (GIS) knowledge. Maps produced by `rmap` are `ggplot` objects and thus capitalize on the flexibility and advancements of the `ggplot2` package and all elements of each map are thus fully customizable. Additionally `rmap` automatically detects and produces comparison maps if the data has multiple scenarios or time periods as well as animations for time series data. Advanced users can load their own shapefiles if desired. `rmap` comes with a range of pre-built color palettes but users can also provide any `R` color palette or create their own as needed. Four different legend types are available to highlight different kinds of data distributions. The input spatial data can be both gridded or polygon data. `rmap` is desgined in particular for comparing spatial data across scenarios and time periods and comes preloaded with standard country, state, and basin maps as well as custom maps compatible with the Global Change Analysis Model (GCAM) spatial boundaries. `rmap` has a growing number of users and its products have been used in multiple multisector dynamics publications as well as a required dependency in other R packages such as `rfasst` and `metis`. `rmap's` automatic processing of tabular data using pre-built map selection, difference map calculations, faceting, and animations offers unique functionality which makes it a powerful and yet simple tool for users looking to explore multi-sector, multi-scenario data across space and time.

58 GEOSCIENCES↗

tell: a Python package to model future total electricity loads in the United States

The purpose of the Total ELectricity Load (tell) model is to generate 21st century profiles of hourly electricity load (demand) across the Conterminous United States (CONUS). tell loads reflect the impact of climate and socioeconomic change at a spatial and temporal resolution adequate for input to an electricity grid operations model. tell uses machine learning to develop profiles that are driven by projections of climate/meteorology and population. tell also harmonizes its results with United States (U.S.) state-level, annual projections from a national- to global-scale energy-economy model. This model accounts for a wide range of other factors affecting electricity demand, including technology change in the building sector, energy prices, and demand elasticities, which stems from model coupling with the U.S. version of the Global Change Analysis Model (GCAM-USA). tell was developed as part of the Integrated Multisector Multiscale Modeling (IM3) project. IM3 explores the vulnerability and resilience of interacting energy, water, land, and urban systems in response to compound stressors, such as climate trends, extreme events, population, urbanization, energy system transitions, and technology change

24 POWER TRANSMISSION AND DISTRIBUTION↗

gcamfaostat: An R package to prepare, process, and synthesize FAOSTAT data for global agroeconomic and multisector dynamic modeling

The gcamfaostat R package is designed for the preparation, processing, and synthesis of the Food and Agriculture Organization (FAO) Statistics (FAOSTAT) agroeconomic data. The primary purpose is to facilitate FAOSTAT data use in global economic and multisector dynamic models while ensuring transparency, traceability, and reproducibility. Here, we provide an overview of the development of gcamfaostat (v1.0) and demonstrate its capabilities in generating and maintaining agroeconomic data required for the Global Change Analysis Model (GCAM). Our initiative seeks to enhance the quality and accessibility of data for the global agroeconomic modeling community, with the aim of fostering more robust and harmonized outcomes in a collaborative, efficient, and open-source framework. One of the important features of the package is the possibility to construct the FAO Food Balance Sheets at the disaggregated commodity level (with over 500 commodities), which provides a comprehensive and detailed data input for a variety of analytical and modeling applications. The processed data and visualizations offered by gcamfaostat can also be valuable to a broader audience interested in gaining insights into the intricacies of global agriculture.

97 MATHEMATICS AND COMPUTING↗

Core Model Proposal #350: Detailed Natural Gas Trade

This core model proposal adds detail to GCAM's natural gas trade model by separating gas trade between Liquefied Natural Gas (LNG) and six regional gas pipeline networks. This entails creating new trade markets for global LNG and the six regional pipeline networks, and modifying the regional natural gas sector in each region to reflect the competition between LNG and pipelines within imported natural gas, and between different pipeline networks. The nesting subsector capability (CMP #299 Infinite Subsector Nesting) was used to represent this additional level of competition on the consumption side.

03 NATURAL GAS↗

Core Model Proposal #359: Hydrogen and Transportation Technology Update

This core model proposal updates modeling structures, key technology assumptions, and data sources for a detailed representation of hydrogen in GCAM’s energy system, including production, transmission and distribution, and consumption in hydrogen end-use sectors (buildings, transportation, industry).

08 HYDROGEN↗

Global Land Use under SSP-RCP scenarios

This dataset includes global land use projections at 0.05-degree resolution under 15 SSP-RCP scenarios in the 21st century, produced by GCAM and Demeter and forced by five global climate models.

54 ENVIRONMENTAL SCIENCES↗

Global land use for 2015-2100 at 0.05° resolution under diverse socioeconomic and climate scenarios

Global future land use (LU) is an important input for Earth system models for projecting Earth system dynamics and is critical for many modeling studies on future global change. Here we generated a new global gridded LU dataset using the Global Change Analysis Model (GCAM) and a land use spatial downscaling model, named Demeter, under the five Shared Socioeconomic Pathways (SSPs) and four Representative Concentration Pathways (RCPs) scenarios. Compared to existing similar datasets, the presented dataset has a higher spatial resolution (0.05°×0.05°) and spreads under a more comprehensive set of SSP-RCP scenarios (in total 15 scenarios), and considers uncertainties from the forcing climates. We compared our dataset with the Land Use Harmonization version 2 (LUH2) dataset and found our results are in general spatially consistent with LUH2. The presented dataset will be useful for global Earth system modeling studies, especially for the analysis of the impacts of land use and land cover change and socioeconomics, as well as the characterizing the uncertainties associated with these impacts.

54 ENVIRONMENTAL SCIENCES↗

iiasa/emissions_downscaling

This software tool, written in R, performs manipulations necessary to translate Human-Earth system model regional/sector emission outputs, such as those from PNNL's Global Change Assessment Model (GCAM), to gridded form needed by Earth System Models, such as DOEs E3SM (Energy Exascale Earth System Model) model. The tool takes as its primary input emissions in a standard community format, and produces CF-compliant netCDF gridded emission as its primary output. As intermediate output the tool produces downscaled emissions at the country level.

Feng, Leyang↗

Relative Cost-Effectiveness of Electricity and Transportation Policies as a Means to Reduce CO2 Emissions in the United States: A Multi-Model Assessment

Two common energy policy instruments in the United States are tax incentives and technology standards. Although these instruments have been shown to be less cost-effective as a means to reduce CO2 emissions than direct emissions pricing mechanisms, it can be challenging to compare the CO2 emissions reduction costs of such policies across sectors, given the wide range in estimates for any given policy and inconsistencies in how such estimates are constructed across studies. This study addresses this analytical gap by simultaneously comparing the cost-effectiveness of policies across the electricity and transportation sectors using three publicly available US energy system models (EM-NEMS, ReEDS, and GCAM-USA). Four policies are explicitly compared: wind and solar tax credits, a renewable portfolio standard (RPS), a renewable fuel standard (RFS), and an electric vehicle (EV) tax credit. An economy-wide carbon tax is used as a benchmark for cost-effectiveness. Results from this study confirm prior insights about the cost-effectiveness of economy-wide carbon pricing relative to sectoral instruments but also reveal several novel insights about particular sectoral policies. Specifically, this study finds that (1) current electricity tax incentives provide uneven support for wind and solar technologies, (2) despite known inefficiencies, renewable energy policies in the electricity sector are less expensive than earlier estimates due to technology advancement and changes in market conditions, (3) within transportation, an expanded RFS with increasing advanced biofuel targets is more cost-effective than an EV tax credit extension under plausible assumptions, (4) EV incentives lead to a rebound in conventional vehicle fuel economy that further erodes cost-effectiveness, and (5) the change in policy costs over time is not known a priori, but the relative cost ordering among these policies does not depend on the timeframe of analysis. These results are largely robust to the underlying modeling framework, increasing the confidence with which they can be applied to climate policy evaluation.

economics↗

Xanthos Output Dataset Under ISIMIP3b Selected CMIP6 Scenarios: 1850 - 2100 (Basin and Regional Scale)

Xanthos is an open-source hydrologic model, written in Python, designed to quantify and analyze global water availability. Xanthos simulates historical and future global water availability on a monthly time step at a spatial resolution of 0.5 geographic degrees. Xanthos was designed to be extensible and used by scientists that study global water supply and work with the Global Change Analysis Model (GCAM). Xanthos Output Dataset This dataset includes Xanthos outputs forced by the ISIMIP3b bias-adjusted CMIP6 climate forcing (1850 - 2100). There are 30 scenarios consists of 10 CMIP6 GCMs and 3 SSP-RCP scenarios. (1) 10 CMIP6 GCMs: GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL, CanESM5, CNRM-CM6-1, CNRM-ESM2-1, EC-Earth3, and MIROC6 (2) 3 SSP-RCP Scenarios: SSP1-2.6, SSP3-7.0, and SSP5-8.5 The output variables in this dataset are aggregated to basin and regional scales. (1) Monthly runoff at basin scale (2) Annual accessible renewable water at basin scale (2) Annual actual hydropower production, hydropower potential, and exploitable hydropower potential at regional scale

Climate Change↗

Model run output for WM-ABM 2024 HESS Publication

Model output for computational experiments introducing WM-ABM. See associated HESS publication here: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1604/ all_nldas_cdl_data_v3.txt - Cropland Data Layer data aggregated to 1/8 degree resolution for input into the PMP calibration process. Variables include NLDAS ID, crop type, year, the count of pixels in the grid cell of the crop type, CDL crop ID, and GCAM crop ID. 20230115 ABM runs - WM-ABM model outputs for various scenarios described in HESS publication. If multiple versions exist (e.g., v2, v3, etc.) latest version indicates dataset generated for manuscript. The "baseline" run is the primary run, where as the "memXX" runs indicate sensitivity runs testing different parameters of agent memory. Output files starting with "abm_results_" include groundwater irrigated acres and surface water irrigated acres for each farm/agent combination. Other CSV outputs provide summary statistics of various agent and water shortage outcomes, used for generation of figures in the HESS manuscript.

ABM↗

From Here to There - Bird's Eye Perspective on Grant Lifecycle and Administration

“What gets measured gets done ”or “measure it to manage it”. The statements have become matter of fact. However, measurement (and even metrics) alone cannot fully communicate an organization’s story without the point of accountability. As a dynamic org, we must measure what is necessary, using the appropriate lenses to analyze the most relevant information that allows robust decision-making. A key function of Program Planning and Control (PP&C) is to assist HRP in the development of such strategy that ensures the proper execution of program research goals. With introspection of the Technical Officer role as defined by 2 CFR(i.e., Part 200, Part 1800)and subsequent governance under the NASA Grant and Cooperative Agreement Manual (GCAM), we’ve realized an obvious disconnect among Principal Investigator’s (PI),technical communication with the elements and some of the administrative/fiduciary requirements that should run parallel to the science work. This find has introduced new program risks as evidenced through incomplete deliverables or other less-than-successful requirements through the lifecycle for work we fund. One solution the Grants Technical Officer proposes is for PP&C to share in the communication with stakeholders of grants administration much earlier in the award process in attempt to reduce programmatic risks; particularly in data, reporting and performance management. As such, the planned IWS 2022 session will focus on(plenary) topics that include (1) definition of the grant lifecycle, (2) anatomy of a grant(NF1687 the award document), and (3)performance measurement/post-award monitoring. As HH&P pursues its strategic objectives to evolve the directorate into a data rich and knowledge rich organization, it has become increasingly important to develop the multiple levels of insight across the program to ensure data and other reporting(qualitative/quantitative), alongside proper administration. And the desired outcome is to make certain HRP meets/exceeds the accountability standard that our research information is received, maintained and accessible in posterity.

Lucy D Barnes-Moten↗

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Prospective Impact Analysis of Novel Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics. The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM (Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

decarbonizing↗

Towards Prospective LCA Using Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) Framework for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM(Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

emissions↗

Evaluating long-term emission impacts of large-scale electric vehicle deployment in the US using a human-Earth systems model

While large-scale adoption of electric vehicles (EVs) globally would reduce carbon dioxide (CO 2 ) and traditional air pollutant emissions from the transportation sector, emissions from the electric sector, refineries, and potentially other sources would change in response. Here, a multi-sector human-Earth systems model is used to evaluate the net long-term emission implications of large-scale EV adoption in the US over widely differing pathways of the evolution of the electric sector. Our results indicate that high EV adoption would decrease net CO 2 emissions through 2050, even for a scenario where all electric sector capacity additions through 2050 are fossil fuel technologies. Greater net CO 2 reductions would be realized for scenarios that emphasize renewables or decarbonization of electricity production. Net air pollutant emission changes in 2050 are relatively small compared to expected overall decreases from recent levels to 2050. States participating in the Regional Greenhouse Gas Initiative experience greater CO 2 and air pollutant reductions on a percentage basis. These results suggest that coordinated, multi-sector planning can greatly enhance the climate and environmental benefits of EVs. Additional factors are identified that influence the net emission impacts of EVs, including the retirement of coal capacity, refinery operations under reduced gasoline demands, and price-induced fuel switching in residential heating and in the industrial sector.

54 ENVIRONMENTAL SCIENCES↗