Download information Please do not request a data download here. Rather, the data is available for download at the NGFS Scenario Explorer under this download link: https://data.ece.iiasa.ac.at/ngfs/#/downloads. In order to download click on Guest login. You will be forwarded to the downloads page where you find the data. The license permits use of the scenario ensemble for scientific research and commercial use, but restricts redistribution of substantial parts of the data. Please refer to the FAQ and legal code for more information. Release notes V4.0 An effort has been made to keep the NGFS Phase 4 data model as much as possible in line with Phase 3 as possible. Nonetheless, there are a few changes: The MESSAGE model (IAM and Downscaling) does not report GDP for the Low demand scenario because the demand projections are developed bottom-up uncoupled from the GDP feedbacks in the MESSAGEix framework. The *Divergent net Zero* scenario was dropped. Two new scenarios were introduced: Fragmented World Low Demand There's now a full set of From the MAGICC climate model, we now report more percentiles ranging from 5th to 95th instead of just the previously only the 50th percentile. The in Phase 3 from REMIND and GCAM model-reported temperature variable Temperature|Global mean has been dropped. The replacement variable is the more accurate AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|50.0th. In addition we also report atmospheric concentrations from MAGICC for CO2, CH4, and N2O. The Damages post processing was moved under the Downscaling model as it reports country level data. Previously it was filed under the native IAM models.The variables affected are: GDP|PPP|including medium chronic physical risk damage estimate GDP|PPP|including high chronic physical risk damage estimate Post-processed|high GDP change|KW panel population-weighted|GMT AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|5.0th Percentile Post-processed|high GDP change|KW panel population-weighted|GMT AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|50.0th Percentile Post-processed|high GDP change|KW panel population-weighted|GMT AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|95.0th Percentile Post-processed|median GDP change|KW panel population-weighted|GMT AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|5.0th Percentile Post-processed|median GDP change|KW panel population-weighted|GMT AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|50.0th Percentile Post-processed|median GDP change|KW panel population-weighted|GMT AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|95.0th Percentile net GDP|PPP|high damage|KW panel population-weighted|GMT AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|5.0th Percentile net GDP|PPP|high damage|KW panel population-weighted|GMT AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|50.0th Percentile net GDP|PPP|high damage|KW panel population-weighted|GMT AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|95.0th Percentile net GDP|PPP|median damage|KW panel population-weighted|GMT AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|5.0th Percentile net GDP|PPP|median damage|KW panel population-weighted|GMT AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|50.0th Percentile net GDP|PPP|median damage|KW panel population-weighted|GMT AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|95.0th Percentile Country Temperature|Downscaling|5.0th Percentile Country Temperature|Downscaling|50.0th Percentile Country Temperature|Downscaling|95.0th Percentile In Phase 3 for the integrated damage runs of REMIND (REMIND-MAgPIE 3.0-4.4 IntegratedPhysicalDamages (95th-high) and REMIND-MAgPIE 3.0-4.4 IntegratedPhysicalDamages (median)) the variable GDP|PPP|Counterfactual without damage was reported erroneously. It has been removed for this release. Due to limitation in Excel data size the Downscaling data have now been split into three files, one for each IAM. In addition, due to a processing error, NiGEM data for the MESSAGE model, for scenarios Net Zero 2050, Below 2C and Fragmented World are currently not published. Work is being done to fix this as soon as possible. About NGFS The Network for Greening the Financial System (NGFS) is a group of 127 central banks and supervisors and 20 observers committed to sharing best practices, contributing to the development of climate– and environment–related risk management in the financial sector and mobilising mainstream finance to support the transition toward a sustainable economy. This Scenario Explorer is a web-based user interface for NGFS Scenarios. This provides intuitive visualizations & display of time series data and download of the data in multiple formats. NGFS scenarios were produced by NGFS Workstream on Scenarios Design and Analysis in partnership with an academic consortium from the Potsdam Institute for Climate Impact Research (PIK), International Institute for Applied Systems Analysis (IIASA), University of Maryland (UMD), Climate Analytics (CA), and the National Institute of Economic and Social Research (NIESR). This work was made possible by grants from Bloomberg Philanthropies and ClimateWorks Foundation. The bespoke scenarios developed in Phase 4 of this project are generated by state-of-the-art well-established integrated assessment models (IAMs), namely GCAM, MESSAGE-GLOBIOM and REMIND-MAgPIE, as well as the NiGEM macroeconomic model.
Accelerating the deployment of renewable energy renewable energy (RE) is one of the most important strategies to achieve the 2060 carbon neutrality goal in China. In this context, it is crucial to understand the RE investment needs at the provincial level to better allocate resources and develop policies to facilitate RE development and deployment at the local level. In this paper, we estimate the wind and solar investment needs by Chinese provinces between 2020 and 2060 under four alternative pathways towards China’s 2060 carbon neutrality, by using a global integrated assessment model with provincial details of China combined with the most updated cost data for each province, and explicitly considering national and local investment market conditions. Results show that the average annual wind and solar investment needs are $317 billion per year between 2020 and 2060, or 2.3 percent of China’s 2020 GDP. We find large spatial and temporal variations of the needed RE investment and identify that technologies, resource endowment, and financial conditions are the three primary contributions to the regional disparity in investment needs. This study delves into the local factors constraining RE deployment in China, providing insights applicable not only to the country but also holding implications for studying global RE investment dynamics in alignment with the collective pursuit of heightened clean energy transition goals.
Abstract China has large, estimated potential for direct air carbon capture and storage (DACCS) but its deployment locations and impacts at the subnational scale remain unclear. This is largely because higher spatial resolution studies on carbon dioxide removal (CDR) in China have focused mainly on bioenergy with carbon capture and storage. This study uses a spatially detailed integrated energy-economy-climate model to evaluate DACCS for 31 provinces in China as the country pursues its goal of climate neutrality by 2060. We find that DACCS could expand China’s negative emissions capacity, particularly under sustainability-minded limits on bioenergy supply that are informed by bottom-up studies. But providing low-carbon electricity for multiple GtCO 2 yr −1 DACCS may require over 600 GW of additional wind and solar capacity nationwide and comprise up to 30% of electricity demand in China’s northern provinces. Investment requirements for DACCS range from $330 to $530 billion by 2060 but could be repaid manyfold in the form of avoided mitigation costs, which DACCS deployment could reduce by up to $6 trillion over the same period. Enhanced efforts to lower residual CO 2 emissions that must be offset with CDR under a net-zero paradigm reduce but do not eliminate the use of DACCS for mitigation. For decision-makers and the energy-economy models guiding them, our results highlight the value of expanding beyond the current reliance on biomass for negative emissions in China.
Download information Please do not request a data download here. Rather, the data is available for download at the NGFS Scenario Explorer under this download link: https://data.ece.iiasa.ac.at/ngfs-phase-3/#/downloads. The license permits use of the scenario ensemble for scientific research and commercial use, but restricts redistribution of substantial parts of the data. Please refer to the FAQ and legal code for more information. 3.2 (9 September 2022) Added "Baseline" scenario for NiGEM Moved MESSAGEix-GLOBIOM 1.1-M-R12 variables that were wrongly filed under "Downscaling [MESSAGEix-GLOBIOM 1.1-M-R12]" back to model native. About the data set This dataset contains a set of climate scenario that have been developed for the Network for Greening the Financial System (NGFS). The NGFS is a group of 83 central banks and supervisors and 12 observers committed to sharing best practices, contributing to the development of climate– and environment–related risk management in the financial sector and mobilising mainstream finance to support the transition toward a sustainable economy. The scenarios in this dataset were produced by NGFS Workstream 3 in partnership with an academic consortium from the Potsdam Institute for Climate Impact Research (PIK), International Institute for Applied Systems Analysis (IIASA), University of Maryland (UMD), Climate Analytics (CA), the Eidgenössische Technische Hochschule Zürich (ETH) and the National Institute of Economic and Social Research (NIESR). The Phase 3 bespoke scenarios are generated by state-of-the-art well-established integrated assessment models (IAMs), namely GCAM, MESSAGEix-GLOBIOM and REMIND-MAgPIE. These models allow the estimation of global and regional mitigation costs, the analysis of energy system transition characteristics, the quantification of investments required to transform the energy system, and the identification of synergies and trade-off of sustainable development pathways. Technical documentation is available to help users access the datasets. The documentation describes the models and variables, as well as provides detailed guidance for database users. Scenario presentation materials and the user guide are also available at the NGFS portal.
Download information Please do not request a data download here. Rather, the data is available for download at the NGFS Scenario Explorer under this download link: https://data.ece.iiasa.ac.at/ngfs-phase-3/#/downloads. The license permits use of the scenario ensemble for scientific research and commercial use, but restricts redistribution of substantial parts of the data. Please refer to the FAQ and legal code for more information. 3.3 (7 October 2022) Correction notice: In this version of the NGFS Phase 3 data set, V3.3, the updates for the NiGEM model were not included due to a processing error. This has been rectified is the next version, V3.4 (10.5281/zenodo.7198430). For reasons of transparency and continuity we have chosen to keep the release notes below regarding NiGEM even though they are not correct. The rest of the updates were included correctly. General: Included all years in the data set, including years passed. NiGEM: (not included due to processing error) Removed incorrect "index; 2017=100" from NiGEM units. Units affected are (updated unit in bold): index; 2017=100 US$ per barrel index; 2017=100 US$ per barrel(equiv) % difference, index; 2017=100 US$ per barrel % difference, index; 2017=100 US$ per barrel(equiv) Transition effects now refer to the combined carbon pricing and recycling shocks rather than the carbon price only. Transition effects have been removed for "Current Policies" which only experiences chronic physical impacts (no carbon pricing under current policies). Combined effects now equal transition + physical. A further combined data row has been added to the disorderly scenarios ("Delayed transition", "Divergent Net Zero") to show the output of an additional business confidence shock applied to the combined shock (see scenario description). These data are denoted by the "|Combined plus business confidence" suffix in the variable name. Acute physical risk data have been added for the scenarios "Delayed transition", "Current Policies", "Net Zero 2050" for the world level. As these data are model independent, they have been added under "NiGEM NGFS v1.22" only (without any input model information). Country specific currency information for units labelled as "local currency" has been added to the downloadable data under the "unit" column. It is added as a suffix e.g. "2017 prices; local currency ( US$ Bn)", when before it was "2017 prices; local currency ( US$ Bn)". REMIND integrated damage runs: Added the variables "GDP|PPP|Counterfactual without Damage" and "GDP|PPP|including chronic physical risk damage estimate" for the for the regions "TWN", "MAC" and "CHN". About the data set This dataset contains a set of climate scenario that have been developed for the Network for Greening the Financial System (NGFS). The NGFS is a group of 83 central banks and supervisors and 12 observers committed to sharing best practices, contributing to the development of climate– and environment–related risk management in the financial sector and mobilising mainstream finance to support the transition toward a sustainable economy. The scenarios in this dataset were produced by NGFS Workstream 3 in partnership with an academic consortium from the Potsdam Institute for Climate Impact Research (PIK), International Institute for Applied Systems Analysis (IIASA), University of Maryland (UMD), Climate Analytics (CA), the Eidgenössische Technische Hochschule Zürich (ETH) and the National Institute of Economic and Social Research (NIESR). The Phase 3 bespoke scenarios are generated by state-of-the-art well-established integrated assessment models (IAMs), namely GCAM, MESSAGEix-GLOBIOM and REMIND-MAgPIE. These models allow the estimation of global and regional mitigation costs, the analysis of energy system transition characteristics, the quantification of investments required to transform the energy system, and the identification of synergies and trade-off of sustainable development pathways. Technical documentation is available to help users access the datasets. The documentation describes the models and variables, as well as provides detailed guidance for database users. Scenario presentation materials and the user guide are also available at the NGFS portal.
Demand for agricultural products is an important problem in global change economics. Food consumption will shape and be shaped by global change through interactions with bioenergy and afforestation, two critical issues in meeting international goals. We develop a model of food demand for staple and nonstaple commodities that evolves with changing incomes and prices. The model addresses a long-standing issue in estimating food demands, the evolution of demand relationships across large changes in income and prices. We discuss the model, some of its properties and limitations. We estimate parameter values using pooled cross-sectional-time-series observations and Bayesian Monte Carlo method and cross-validate the model by estimating parameters using a subset of the observations and test its ability to project into the unused observations. Finally, we apply bias correction techniques borrowed from the Earth system modeling community and report results. We find that the demand for food rises rapidly as income initially increases from zero. Demand for staples peaks at under $1000 per person per capita. Nonstaple food demands increase steadily with income. While staples are an inferior good at per capita incomes greater than $1000, we see no evidence that there is a range of per capita income for which staples are Giffen goods.
Deep decarbonization paths to the 1.5 °C or 2 °C temperature stabilization futures require a rapid reduction in coal-fired power plants, but many countries are continuing to build new ones. Coal-fired plants are also a major contributor to air pollution related health impacts. Here, we couple an integrated human-earth system model (GCAM) with an air quality model (TM5-FASST) to examine regional health co-benefits from cancelling new coal-fired plants worldwide. Our analysis considers the evolution of pollutants control based on coal plants vintage and regional policies. We find that cancelling all new proposed projects would decrease air pollution related premature mortality between 101,388–213,205 deaths (2–5%) in 2030, and 213,414–373,054 (5–8%) in 2050, globally, but heavily concentrated in developing Asia. These health co-benefits are comparable in magnitude to the values obtained by implementing the Nationally Determined Contributions (NDCs). Furthermore, we estimate that strengthening the climate target from 2 °C to 1.5 °C would avoid 326,351 additional mortalities in 2030, of which 251,011 (75%) are attributable to the incremental coal plant shutdown.
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