The analysis shown here is based on the resource efficiency and climate change (RECC) framework 26, which tracks material cycles over time and links the services provided (individual motorized transport and shelter) to the operation of in-use stocks of products (passenger vehicles and residential buildings). It calculates the expansion and maintenance of products in use over time, and estimates the material production needed and the potential for recycling. A key innovation of RECC is the upscaling of representative single product descriptions (‘archetypes’) with different degrees of material and energy efficiency. The product archetypes were simulated with engineering tools that model building energy balance and vehicle driving cycles. We define future scenarios for passenger vehicle and residential building operation by augmenting the storylines of the shared socioeconomic pathways 32 to describe future service demand in the passenger vehicle fleet and residential buildings and calculate associated material requirements, covering the entire globe in 20 countries/regions until 2060. The GHG mitigation potential of ten material efficiency strategies at different stages of the material cycle is quantified by ramping up their implementation rates to the identified technical potentials by 2040. Each material efficiency strategy can be implemented separately or as part of a cascade of strategies. The model allows for calculating the impact of one strategy at a time (which is mostly used for sensitivity analysis) or a bundle of strategies in different orders of implementation, each for different socioeconomic and climate policy scenarios.
The RECC model framework
Services are linked to material cycles via a stock-flow-service nexus 38 (Fig. SI1-1). The scheme shown in this figure is based on the energy service cascade 39 that relates human values to services to functions to products (and their operation). From future scenarios for providing functions to people (like transport-km), we determined the amount of vehicles and building area needed in a given year, and used RECC routines for stock-driven modelling 40 to translate product in-use stock demand into production of new and recycling of old products. Using product material composition data that are part of the product archetype description, RECC converts the flows of new and old products into material flows, thus representing a dynamic material flow analysis (MFA) 41,42. It also calculates all energy demand from material production, recycling, and product operation, and estimates related GHG emissions via environmental extensions as done in previous work 43.
Overall, RECC generates a set of what-if scenarios 44 for material efficiency in the vehicle and building sectors and the related major material cycles against different socioeconomic, technological, and climate-political backgrounds 31. RECC does not assess the likelihood of realization of any of the scenarios studied but checks if mass balance constraints (e.g. by long product lifetimes or limited scrap supply) render some scenarios unfeasible from a material cycle point of view.
The RECC Database
The RECC v2.4 database contains 104 model parameters. Parameters range from static values (direct emissions of combustion per MJ of energy carrier) to highly detailed and uncertain and thus scenario-dependent datasets (e.g., the future energy carrier split of buildings by region, time, and demand (heating/cooling/hot water).
The RECC database was compiled as a community effort involving many experts. Its scope is unprecedented in the industrial ecology community. Data templates and project-wide classifications were used to facilitate the compilation of the various types of information. Depending on data availability, we applied several pathways of data compilation:
- Extract mostly socioeconomic parameters from existing scenario models (scenario reference)
- Compile own plausible scenario estimates for socioeconomic parameters in line with the different scenario narratives where results from established model frameworks are not available (group consensus scenarios)
- Extract process-, product, and material-specific data from the engineering and industrial ecology literature (‘bottom-up’ data)
- Extract quantitative estimates of resource efficiency strategy potentials, mostly related to prototypes and case studies, from the literature (strategy potentials)
- Simulate energy consumption and material composition of building and vehicle archetypes with specialized software, which are then used as bottom-up product descriptions with and without implementation of ME strategies (product archetype descriptions)
Scenario reference and group consensus storyline extension
For the socioeconomic parameters, the Shared Socioeconomic Pathways (SSP) database and model results as well as available data from the World Energy Outlook and Energy Technology Perspectives models were used wherever possible, e.g., for future population, future GHG intensity of energy supply, or share of electric and hybrid vehicles 29,31,45. Data were parsed and reviewed by the RECC team, then aggregated, disaggregated, and/or interpolated to fit the project-wide classification. For each parameter file the data gathering process is documented both in the respective template files in the RECC database (whenever only Excel was used), in custom scripts (for more comprehensive datasets that required pre-processing).
For some parameters like the future stock levels or the split of residential buildings into different types, no detailed SSP-consistent scenario calculation was available that we could refer to. Hence, we assumed a set of plausible target values for a number of socioeconomic parameters in line with the storylines of the individual socioeconomic scenarios, fully documented by Fishman et al. 32. This process has been used when translating broad storylines into parameters with high product- and regional resolution and sector-specificity, cf. Riahi et al. 31 and Grübler et al. 28. The chosen target values for 2020, 2030, 2040, 2050, and 2060 and the rationale for their choice are documented in scenario target tables, one for each parameter. An overview is given in Table 1.
Table 1: RECC framework data aspects and their resolution.
Model and data aspect
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Resolution
|
Time
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2016-2060 in steps of 1 year
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Age-cohorts/Vintages
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Vehicles: 1980-2060, residential buildings: 1900-2060
|
Regions
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20 countries and world regions, covering the entire world.
|
Products
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6 passenger vehicle drive technologies (with in total 48 vehicle archetypes), 13 residential building types with in total 52 archetypes
|
Engineering materials
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construction grade steel, automotive steel, stainless steel, cast iron, wrought Al, cast Al, copper electric grade, plastics, wood and wood products, zinc, cement and concrete aggregates
|
Waste and scrap types
|
heavy melt, plate, and structural steel scrap; steel shred; Al extrusion scrap, auto rims, clean; Al old sheet and construction waste; Al old cast; copper wire scrap; construction waste, concrete, bricks, tiles, ceramics
|
Chemical elements
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C, Al, Cr, Fe, Cu, Zn, ‘other’
|
Energy carriers
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Electricity, coal, hard coal, diesel, gasoline, natural gas, hydrogen, fuel wood
|
Service categories
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Driving (vehicles), heating, cooling, domestic hot water (residential buildings)
|
Scenarios
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Socioeconomic: Low energy demand (LED), SSP1, SSP2
Climate policy: No policy after 2020 (reference scenario), 2 degrees Celsius (66%), corresponding to RCP2.6 forcing pathway.
|
Bottom-up data on technology
For the energy intensity, emissions intensity, and material composition of products and processes, detailed but representative product or process descriptions were compiled from the literature and available databases. These data include the material composition and specific energy consumption of vehicles and buildings 46–48, e.g., the loss and recovery rates for the manufacturing and waste management industries, e.g., 43,49, and the specific energy consumption and process emissions for the manufacturing, waste management, and primary material production industries 45,50. While the data can be regarded as representative of current average global technology, their main limitation is that they are static and no information on their change under different socioeconomic and climate policy scenarios is given. To become more realistic, a scenario reference was made wherever possible (cf. above), e.g., for the changing GHG intensity of the supply of different energy carriers, for which a combination of MESSAGE IAM 31 results and IEA Energy Technology Perspective results 45 was used. Also, for the average GHG intensity of primary metal production, emissions from ecoinvent 50 were updated to take into account scenario-dependent changes of the GHG intensity of electricity generation.
For some material efficiency parameters, including the improvement potentials for fabrication scrap, end-of-life recovery efficiency of scrap, re-use of steel components in buildings, or product lifetime extension, previous estimates can be used 43,51.
Building and vehicle archetype descriptions
Here, ‘archetype’ refers to an idealized representative and scalable description of the physical properties (energy intensity of operation and material composition) of a product with a certain functionality, assuming typical user behavior in a given region.
For passenger vehicles, drive technology, segment (car size), and material design choice together determine the archetypes’ material composition, and the three properties above plus the assumed driving cycle determine its specific operational energy consumption (specific = per km driven).
For residential building, building type, energy standard, material intensity (conventional or lightweight design), material design choice, and stylized climate conditions (heating and cooling degree days by region) together determine the archetypes’ material composition and specific operational energy consumption (specific = per m2).
For the final product categories residential buildings and vehicles, the product-specific simulation tools BuildME (https://github.com/nheeren/BuildME), GREET (https://greet.es.anl.gov/) and FASTSim (https://www.nrel.gov/transportation/fastsim.html) were used to model the archetype descriptions by deriving model estimates for both the material composition and energy intensity of operation for different building and vehicle configurations. For each of the nine building and six vehicle types, four archetypes, representing maximal potential for change, were simulated: a standard product without special consideration of material efficiency, downsizing, or material substitution, a downsized product, a product with ambitious material substitution, and a downsized material-substituted product.
Open science project
The RECC framework is open (https://github.com/YaleCIE/RECC-ODYM) and modular. Third parties can modify the scenario assumptions and to run calculations with custom parameters and storylines. A detailed description and definition of all model aspects, the classifications used for them, the system variables and parameters, the model equation and their division into modules and the data compilation, (dis)aggregation and formatting process is contained in the model documentation (supplementary material 2). The project’s entire database is archived on Zenodo (dataset DOIs [to be inserted for final publication]).