Key model improvements in the IMAGE 3.3 model

General	The model has been fully calibrated up to 2015, and where possible, even to a more recent date (2018 for energy system variables, 2020 for renewables capacity and CO2 emission data). Moreover, the base year for scenario analysis was set at 2020, implying that scenarios follow the same trajectory in the 2015-2020 period
Energy demand	The model now has a detailed representation of energy demand in transport, industry, buildings, services and agriculture. For transport, the model now describes all relevant transport modes and technologies within these modes based on recent data and innovations (particularly for electric vehicles). Also, the technology costs of electric and hydrogen-fueled transport technologies were updated. Moreover, an explicit representation of energy use of gas pipelines was added and calibrated to IEA data. Finally, also a new CNG fuelled car type was added. The model describes steel production, pulp and paper, chemicals and feedstock, cement and food products for industry. All sectors contain disaggregated technology descriptions. Ammonia demand (as part of chemicals) has been linked to agricultural production. The buildings sector represents energy services in the residential sector - including a detailed representation of insulation, early retirement of appliances, as well as variable building lifetimes and building types. The service model describes energy use for heating, cooling, lighting, appliances, and other services in the service sector. For buildings, also an explicit representation of insulation levels and renovations for the housing stock was added. In addition, heat pumps were added as an additional heating technology. The agriculture sector, finally, describes energy use for irrigation and other functions.
Energy conversion	Details were added to electricity representation and hydrogen production (using a residual load duration curve approach). The data on existing plants was updated. In addition, hydropower modelling was made dynamic (instead of a prescribed fraction of total potential) using the information on potential and cost via cost curves. Also, an improved technological learning formulation was introduced. Finally, rooftop PV was added as an additional form of PV power supply.
Energy supply	Bioenergy modelling was greatly improved, using dynamic land-use change emission factors based on the IMAGE-land model and adding biofuel production with carbon capture and storage technology routes. Moreover, a BECCS option was added to liquid biofuel production. The data on fossil fuel reserves and resources was updated. A hydrogen option was added for electricity and secondary heat generation. Direct renewable and baseload electrolyzers are now included. Climate impacts on different forms of renewable energy (solar, wind, hydropower and bioenergy) were added to the model. Different discount rate options (including endogenous financial learning) are now included.
Land use	In land use, the number of crop categories was increased to 16 representing all crop production reported by the FAO: wheat, rice, maize, tropical cereals, other temperate cereals, pulses, soybeans, temperate oil crops, tropical oil crops, temperate roots & tubers, tropical roots & tubers, sugar crops, oil palm, vegetables & fruits, other non-food, luxury crops, spices, plant-based fibres). Deforestation due to other reasons than agricultural expansion was improved based on FAO data in combination with satellite data from ESA-CCI. Anthropogenic land use for other reasons than agriculture or built-up is accounted for. The coupling between IMAGE-land and LPJmL has been updated with MPI software allowing further parallelization and improved run time. In order to report certain biodiversity indicators without the need of running GLOBIO, the Biodiversity Intactness Index (BII) and Mean Species abundance (MSA) indicators have been added in the postprocessing of IMAGE-land.
Food demand and production	The link between the agriculture-economic model MAGNET and the IMAGE model was significantly improved concerning climate change effects and exogenous and endogenous trends in crop yield changes. Changes in food waste can now be modelled in scenario runs.
Land-based mitigation	The modelling of land-based mitigation in IMAGE and MAGNET improved, including avoiding deforestation and afforestation through MAC curves in FAIR and accounting for the interaction non-CO2 mitigation and the agriculture and food system. Greenhouse gas emissions from peatland degradation were included.
Water	The water modelling in IMAGE linked to LPJmL was improved, introducing municipal, energy and industry water demand in LPJmL and making it possible to account for environmental flow requirements.  The external module GLOFRIS modelling river flood risk was improved, and modelling of coastal flood risk added, allowing breakdowns in contributions of i) socio-economic development; ii) climate change (regional sea-level rise) and iii) local land subsidence. The external water quality module modelling nutrient emissions to surface waters and groundwater has been improved, especially the modelling of nutrient emissions from households in cities and rural areas.
Non-CO2	All non-CO2 GHG marginal abatement cost curves were updated based on recent literature.