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Purpose – The purpose of this paper is to investigate the functionality and effectiveness of the Carbon Risk Real Estate Monitor (CRREM tool). The aim of the project, supported by the European Union’s Horizon 2020 research and innovation program, was to develop a broadly accepted tool that provides investors and other stakeholders with a sound basis for the assessment of stranding risks. Design/methodology/approach – The tool calculates the annual carbon emissions (baseline emissions) of a given asset or portfolio and assesses the stranding risks, by making use of science-based decarbonisation pathways. To account for ongoing climate change, the tool considers the effects of grid decarbonisation, as well as the development of heating and cooling-degree days. Findings – The paper provides property-speciﬁccarbon emission pathways, aswell asvaluable insight into state-of-the-art carbon risk assessment and management measures and thereby paves the way towards a low-carbon building stock. Further selected risk indicators at the asset (e.g. costs of greenhouse gas emissions) and aggregated levels (e.g. Carbon Value at Risk) are considered. Research limitations/implications – The approach described in this paper can serve as a model for the realisation of an enhanced tool with respect to other countries, leading to a globally applicable instrument for assessing stranding risks in the commercial real estate sector. Practical implications – The real estate industry is endangered by the downside risks of climate change, leading to potential monetary losses and write-downs. Accordingly, this approach enables stakeholders to assess the exposure of their assets to stranding risks, based on energy and emission data. Social implications – The CRREM tool reduces investor uncertainty and offers a viable basis for investment decision-making with regard to stranding risks and retroﬁt planning. Originality/value – The approach pioneers a way to provide investors with a profound stranding risk assessment based on science-based decarbonisation pathways. Keywords Risk management, Commercial real estate, Science-based targets, Decarbonisation, Downscaling, Carbon risk, Stranding risk, Excess emissions. Paper type Research paper © Maximilian M. Spanner and Julia Wein. Published by Emerald Publishing Limited. This article is Journal of European Real Estate published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, Research distribute, translate and create derivative works of this article (for both commercial and non- pp. 277-299 Emerald Publishing Limited commercial purposes), subject to full attribution to the original publication and authors. The full 1753-9269 terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode DOI 10.1108/JERER-05-2020-0031 1. Introduction JERER The Paris climate agreement intends to limit global warming to below 2°C above the pre- 13,3 industrial level. This implies that greenhouse gas emissions (GHG) must peak no later than 2030, and carbon emissions originating from fossil fuels need to be suspended completely by 2070 (UN-FCCC United Nations Framework Convention on Climate Change, 2016). However, an analysis carried out by the United Nations Environmental Program (UNEP United Nations Environmental Program, 2016) indicates that current emission-reduction commitments may result in global warming of more than 3°C, and a “business-as-usual” scenario even to a rise in temperature of more than 4°C by 2100 compared to the pre-industrial level. Research conducted by the European Public Real Estate Association (EPRA) and the European Association for Investors in Non-Listed Real Estate Vehicles (INREV) show that the real estate industry is responsible for approximately 40% of energy used and 29% of all GHG emissions produced within the EU, highlighting the substantial responsibility devolving on this sector. Therefore, the property industry will play a key role in the decarbonisation efforts of the European Union (INREV, EPRA, 2016, 2018). Against this high level of responsibility of the property industry, the refurbishment rates of subsisting buildings are still too low and, due to the long life cycle of buildings, most of the building stock existing in 30 years has already been built, new construction will not be able to compensate for this surplus of GHG emissions. One reason for these low rates might be that for most investors, retroﬁts are still driven primarily by cost-beneﬁt analysis, whereas sustainability-related motivation seems not to play a role at all in the decision- making process (Christensen et al.,2018). On the other hand, barriers to more investment include – besides economic factors and split incentives – a lack of transparency regarding the risks associated with poor energy efﬁciency. Aggravating the European Union’s ﬂagship in terms of climate protection efforts, the Energy Performance of Buildings Directive (EPBD) which demands “nearly-zero-energy” buildings from 2021 onwards, focusses solely on some kind of emission (so-called “regulated emissions”), whereas other so-called “unregulated” emissions are not taken into account. Therefore, international investors have no comparable entity that targets all relevant emissions originating from their building stock and are likely to “get lost” in a mineﬁeld of nationally varying regulations, especially as these unregulated emissions can vary hugely, depending on the usage type. As a survey conducted by the CRREM consortium highlights, the major players within the real estate industry already practice carbon accounting for at least Scope 1 and Scope 2 emissions. However, action-guiding effects are still lacking due to the absence of clear avoidance goals and of information, combined with a lack of carbon prices (Haran et al., 2019). The existing GHG reduction goals are aggregated at excessively high levels and a lack of small-scale data for individual properties on a national or regional level for different types of use can still be observed. This lack of transparency, combined with poor information, bears the risk of ending up in a vicious circle, and the absence of GHG reduction measures, to a risk of capital misallocation. In particular, the actors in the real estate sector require future-oriented data for a focused risk assessment and management. The current possibilities do not provide a viable long-term future outlook. The CRREM project overcomes this lack of information by providing a tool, the Carbon Risk Real Estate Monitor (CRREM tool), that makes use of a downscaling approach which breaks down the mitigation targets to a regional and sectoral diversiﬁed level. This provides the industry with science-based decarbonisation pathways until 2050, giving them the possibility to make their assets and portfolios 1.5-/2-degree-ready (“Paris-proof portfolio”). After entering of the property-speciﬁc input data, the tool calculates the baseline emissions as well as their likely development and setting them in relation to the speciﬁc Carbon risk decarbonisation pathways, so as to determine whether or when the property may face real estate stranding. Additionally, the tool comprises various simulation possibilities like “virtual” monitor retroﬁt decisions and their consequences on ecological performance. The main objective of this paper is to describe the CRREM tool and its functionalities, emphasising the added value for investors and further stakeholders. The remainder of the paper is organised as follows. Section 2 deﬁnes the concept of stranded assets and presents existing approaches for the assessment of a building’s carbon performance. Section 3 explains the general conceptualisation of the CRREM and its speciﬁc understanding of risk. Section 4 describes the technical implementation of the CRREM tool and its derivation of science-based decarbonisation pathways for individual assets. Section 5 contains the functional implementation of the CRREM tool and shows what kind of building data needs to be entered by the user and provides a ﬁrst graphical and numerical assessment. Additionally, this Section introduces user options to make various assumptions of their own. Section 6 presents the results of the risk assessment with respect to ﬁnancial implications and the simulation of future retroﬁt measures. Section 7 concludes and provides a further outlook for potential extensions of the CRREM tool. 2. Background 2.1 Need for carbon risk assessment Research on the impact of climate change on the real estate industry has gained more and more attention in the past years. However, up to now, existing research efforts have focussed almost solely on the potential upside risks of climate change in terms of ﬁnancial payback, green pay-offs or a green premium from higher returns, reduced operating costs or competitive advantages (Eichholtz et al.,2009; Fuerst and Mc. Allister, 2011a; Bienert, 2016). For example, Robinson et al. found that a green pay-off is usually associated with higher energy efﬁciency or other sustainability-related aspects of a property, and the willingness- to-pay for such green ofﬁce buildings was highest for publicly traded ﬁrms and companies in the energy- and information technology (IT)-industry. Especially energy-efﬁcient lighting, efﬁcient electrical and gas heating and cooling are the most broadly accepted and preferred features in the USA (Robinson et al., 2016a). Various studies have found that eco-labels such as leadership in energy and environmental design (LEED) certiﬁcation enhance property performance and add value in terms of higher rents and sales prices (Eichholtz et al., 2010; Fuerst and Mc. Allister, 2011a, 2011b; Kok and Jennen, 2012; Hui et al.,2015)as well as higher hotel revenues (Robinson et al., 2016b). According to Hui et al., LEED certiﬁed ofﬁce buildings in Shanghai, China were found to have a premium of 12.8% on the rental levels compared to non-certiﬁed buildings (Hui et al., 2015). Suh et al. investigated the ofﬁce market in New York City and found that LEED and/ or EnergyStar certiﬁcation even positively inﬂuences the market values of adjoining buildings (Suh et al., 2019). By contrast, no similar effect was found for LEED neighbourhood certiﬁcations (Freybote et al.,2015). Nonetheless, these positive impacts have not generally translated into more sustainable investments and retroﬁts, and this is a major issue in the contemporary environment. When it comes to downside risks, existing research approaches focus predominantly on natural hazards and extreme weather events (Bouwer, 2010; Gasper et al., 2011; Bienert, 2014; Hirsch et al., 2015) or rising sea-levels and the threats of climate change to coastal cities (Hallegatte et al.,2011; Balica et al.,2012; Hallegatte et al., 2013; McAlpine and Porter, 2018; Conyers et al.,2019; Walsh et al.,2019). In contrast, no sufﬁcient focus has been laid on the fact that ongoing climate change JERER might endanger the business case of real estate companies and consequently, the issue of 13,3 “stranded” assets has attracted only minor attention. According to Caldecott, this term which originates from the oil and coal industry indicates that some resources which are currently taken into account in company valuation might have to be revaluated if unexpected shifts, for example demand and price declines or the introduction of a carbon tax, occur (Caldecott, 2018a; Caldecott, 2018b). Applied to the real estate industry, assets can be regarded as stranded as soon as they fail to meet future regulatory requirements and future market expectations in terms of carbon performance (Caldecott et al.,2017). Therefore, as illustrated in Figure 1, stranding can be observed at that point in time when an asset’s carbon performance in terms of GHG intensity per square meter (baseline emissions; black line) hits the maximum allowance (green graph). The only way to overcome stranding is to undertake retroﬁt actions (dashed arrow) and fulﬁl the emission target again. Ideally, the retroﬁt measure will be consistent with Carbon disclosure project (CDP)’s sectoral decarbonisation approach (CDP Carbon Disclosure Project, UN Global Impact, World Resources Institute, WWF, 2015), enabling long-term future-proof performance. Given that consumers are becoming demonstrably more and more environmentally consciousness, and that governments worldwide are passing more stringent regulations, buildings that do not meet market expectations and regulatory guidelines will face a loss in value as well as write-downs and will therefore require major refurbishment activities. Hence, stranding risks need to be accorded the appropriate status in every investor’s risk management process. 2.2 Existing tools and initiative for carbon risk assessment Existing tools for the assessment of carbon risks either focus purely on current emissions instead of future developments and targets, are geographically limited or restricted in terms of asset classes, for example hotel carbon measurement initiative’s “Hotel Footprint Tool” (WTTC, ITP - World Travel and Tourism Council, International Tourism Partnership, 2016) or hotel energy solutions (HES)’“Energy Solution Toolkit” (HES Hotel Energy Solutions, 2011). Other instruments like the “Hotel Water Measurement Initiative tool” (ITP, International Tourism Partnership, 2016), which exclusively targets a hotel property’s water Figure 1. Concept of stranding in the real estate sector footprint, are also very limited in scope and methodology. Additionally, some tools have Carbon risk often not received any signiﬁcant market acceptance and lack public recognition due to a real estate lack of transparency. monitor Tools such as the Carbon Value Analyser, developed by Deutsche unternehmensinitiative energieefﬁzienz (DENEFF) (DENEFF Deutsche Unternehmensinitiative Energieefﬁzienz, Finanzforum Energieefﬁzienz, 2020), provide future prospects and enable an analysis with only seven input parameters for the ﬁnancial assessment of climate change performance. The Carbon Value Analyser benchmarks single commercial assets against a linear reduction 2 2 pathway from 2008 (215 kWh/m ) to 2050 (100 kWh/m ) according to the German energy efﬁciency strategy for buildings [Bundesministerium für Wirtschaft und Energie (BMWi), 2015]. A focus is placed on the ﬁnancial risks and opportunities for a single asset (e.g. change in value, carbon tax and required retroﬁt costs), calculated from the ﬁnancial effects of different policy instruments, to achieve climate and energy-related policy goals. The tool uses ﬁnal energy consumption for the analysis, and the energy consumption considered is based on the deﬁnitions and accounting methods from the Energy Saving Ordinance (EnEv). In contrast to the CRREM tool, restrictions include the limitation to one country and providing an assessment of the stranding risks with only one pathway for the entire commercial real estate sector. Its focus is only asset-based, meaning that an investor cannot assess a multitude of buildings or his whole portfolio at the same time. A portfolio-level analysis is also not provided. The tool certainly provides some valuable initial steps towards the assessment of carbon emissions but is not able to differentiate between different types of usage and does not include detailed reduction pathways. The Carbon Value Analyser only accounts for some basic building features and user-input in terms of energy consumption data is very limited, in fact to only one source of heating supply, no cooling and no fugitive emissions. Ultimately, existing tools have a different focus, for example often only country-speciﬁc, orientate towards future regulatory requirements instead of science-based decarbonisation targets and provide only a short-term outlook or snapshot. By contrast, Global Real Estate Sustainability Benchmark (GRESB)’s environmental, social and governance Benchmark is broadly accepted in the market and deﬁnes high standards with a sound methodology, but focusses only on supplying current emission ﬁgures and benchmarks against other assessment participants (best-in-class approach), instead of providing future decarbonisation pathways or stranding-risk assessment. Additionally, most participants in the GRESB assessment are already amongst the best-in-class assets, whereas other, less sustainable assets are not assessed at all. Despite the analysis of only the best-in- class, the GRESB peers on average still overperform the targets, indicating that even the best assets may not truly comply with the decarbonisation targets. Figure 2 below depicts the performance of all participants in the 2019 GRESB assessment, exemplarily for the UK ofﬁce sector (blue line), against the CRREM decarbonisation requirements (green line, here according to the 1.5°C target). The intersect of the green and blue lines show the point of stranding; from this point onwards the 1.5°C target is no longer met. The percentage of GRESB assets above the GHG intensitytarget are shownbythe redbars in the ﬁgure, also indicating the outperformance of the analysed assets. This analysis conducted by CRREM and GRESB clearly highlights the urgent need for a sound carbon risk assessment methodology and a highly effective tool. 3. Conceptualisation of the carbon risk real estate monitor As outlined above, existing tools mostly set other priorities and do not offer a clear future perspective with decarbonisation pathways enabling investors not only to assess their current emissions, but also future stranding risks. Additionally, to the best of our JERER 13,3 Figure 2. Benchmarking GRESB against the CRREM 1.5°C decarbonisation pathways knowledge, none of the existing tools placed a signiﬁcant focus on the ﬁnancial threats of high carbon emissions but only delivers an asset-snapshot. However, as identiﬁed in many stakeholder-dialogues that the CRREM team conducted, investors require broad information with respect to carbon emissions and stranding risks – both during the acquisition consultation as well as and especially while owning a property. For the purpose of adequate risk management, it is essential that all kind of emissions should be considered. For example, considering only electrical energy use would not be sufﬁcient. Additionally, institutional investors need a tool that differentiates between usage type and location. In summary, besides core aspects like user-friendliness and excellent functionality, a tool which is capable of assessing the carbon risks needs to comply with the following criteria to meet the needs of the property industry: provide science-based decarbonisation pathways for different locations and building types; account for different types of use and locations; incorporate ongoing climate change (shift in Heating Degree Days [HDD]/ Cooling Degree Days [CDD] ) and consider (electricity) grid decarbonisation; provide transparent, comprehensible and reliable calculations; cover all relevant emission sources including cooling and fugitive losses; account for property-speciﬁc anomalies and/or considerations (occupancy rate, varying reporting periods, etc.); enable investors to assess a multitude of properties and portfolios with only one application; focus on at least all European countries, while providing the option for a worldwide roll-out; guarantee the conﬁdential use of user data; sufﬁcient public availability; and ensure maximum market acceptance. The last-mentioned aspect, broad acceptance within the group of institutional investors and Carbon risk other stakeholders, would ensure that a tool deﬁnes a transparent standard for carbon risk real estate assessment, to achieve an optimal capital allocation. With CRREM, we placed particular monitor focus on the involvement of relevant stakeholders by implementing the so-called “European Investor Committee” (EIC) – a group of institutional investors, corporate partners, industry bodies and scientists – accompanying, supporting and advising the project and tool development. The CRREM team regularly asked for feedback and pilot-testing while developing the tool. We thereby wanted to ensure broad acceptance early on. All in all, numerous large international real estate investors tested the tool, which resulted in over four million square metres of lettable space analysed, resulting in a large geographical range and asset-mix. To date, investors and asset managers responsible for more than 3,112 billion euros of Assets under Management (AuM) have made use of the tool. User groups included institutional investors, pension funds, corporates, insurance companies, asset owners, consultants, fund-, asset and investment managers. Further industry and certiﬁcation bodies provided valuable feedback through their expertise on building standards and the issue of carbon risk. The CRREM tool and decarbonisation pathways can be aligned with and incorporated as a metric for operational certiﬁcates or included in sustainability reporting. Lastly, academic research and feedback regarding the methodology and global downscaling targets was presented to various ﬁeld experts. 4. Technical implementation of the “Carbon Risk Real Estate Monitor” tool 4.1 Derivation of property-speciﬁc decarbonisation pathways The CRREM project has derived country and asset-class-speciﬁc decarbonisation and energy reduction pathways, following a stepwise decreasing energy consumption aligned with the requirements of the Paris Agreement to limit global warming to 2°C or 1.5°C by 2050 compared to the pre-industrial level. The CRREM pathways start with the current market average, showing that the mean is underperforming and hence is required to decrease in order not to exceed the carbon budget for the sector until 2050. CRREM estimates that the carbon-intensity of the building sector will globally have to decline from around 52 kgCO e/m /yr to below 10 kgCO e/m /yr by 2050 to comply with the 2-degree 2 2 global carbon budget. These pathways can be regarded as maximum emission allowances (and refer to the green graph in Figure 1 above). CRREM considers the climate impact of different energy sources (so-called carbon intensities) by directly benchmarking a building’s carbon emissions against the CRREM decarbonisation pathways. The target ﬁgure regarding the CRREM decarbonisation pathways is a building’s carbon intensity in terms of annual operational greenhouse gas emissions per gross internal ﬂoor area measured in carbon dioxide equivalent (kgCO e/m ). This includes additional greenhouse gases apart from carbon dioxide. Energy-reduction targets have not been further converted, as the user should be able to use energy readings from the property bills and meters. As in Figure 3 below, eight steps are undertaken to derive the country and building- sector speciﬁc energy-intensity pathways. The initial starting point is the derivation of a global CO e emissions budget consistent with the 1.5°C and 2°C warming targets and reducing this to the proportion for the real estate sector. The global CO e emissions budget for the building sector is 191 GtCO e and 262 GtCO e for the 1.5°C and 2°C targets, 2 2 respectively. The global emissions per ﬂoor area are obtained with the global trajectory of the ﬂoor area for the entire building stock. The underlying data is the global population growth, per capita-ﬂoor-space usage estimates and the increasing building stock deﬁned by the new construction minus demolition. The carbon emissions per ﬂoor area are referred to JERER 13,3 Figure 3. Schematic overview of the CRREM downscaling methodology as the carbon intensity, used for the calculation of the CO e intensity pathway for the building sector. The starting point for the global intensity pathway is at 51.7 kgCO e/m2 in 2018. The downscaling process is continued by the derivation of the EU buildings GHG intensity pathway (including the UK building stock). To derive a country-speciﬁc and use- type-speciﬁc downscaling pathway, CRREM uses each country’s baseline carbon intensity and the assumption of converging carbon intensities until 2050 to the 1.5°C and 2°C targets. CRREM also applies the SDA convergence approach in different steps of the downscaling process. This means that the overall carbon intensity of each country’s building sector converges gradually towards the global average ﬁgure in the deﬁned target year of 2050. In order to calculate the GHG intensity pathways for individual countries and use-types, the Sectoral Decarbonisation Approach (CDP, 2015), each country’s baseline carbon intensity, as well as assumptions of converging carbon intensities at the country level (Figure 4) are used. The EU building ﬂoor area projections are based on the UN Environment and International Energy Agency (2017) global status report. The baseline of 2018 buildings GHG intensities are derived from and based on the average energy mix for each country, property type and respective emission factors. Finally, country-speciﬁc baseline end-energy intensity (kWh/m ) data is used to derive the energy-intensity pathways for individual countries and use-types. Based on projected emission factors and energy mix, typical carbon-to-energy factors have been derived, enabling the conversion of carbon-intensity to energy-intensity ﬁgures. In some cases, grid decarbonisation might progress faster than decarbonisation requirements, which results in no or minimal electricity-reduction requirements. In these cases, CRREM requires energy reductions in line with the UN Sustainable Development Goals of 2.9% per year. A more detailed description of the CRREM downscaling documentation and assessment methodology is available at: www.crrem.org/pathways (CRREM, Carbon Risk Real Estate Monitor, 2020b). 4.2 It-Implementation In order to account for user-friendliness and an easily applicable, functional tool that enables assessing a multitude of properties with data from internal sources, we decided, in close consultation with the involved EIC, to conceive the “Carbon Risk Real Estate Monitor” as a standard ofﬂine MS Excel (.xlsx) ﬁle. Carbon risk real estate monitor Figure 4. Convergence of carbon intensity pathways in different countries The advantage of this structure is that no large server infrastructure or user accounts are necessary and additionally, by using a standardised ofﬁce application, the user beneﬁts from the familiar environment of a widely used ofﬁce program, enabling comprehensive use after only a short period of introduction, while the requirements of the user’s hardware are kept comparatively low. From an IT perspective, the wide usage of Excel and the availability for most operating systems including macOS renders the programming of different versions redundant. Additionally, an excel-based tool instead of an online-based client server system ensures the conﬁdentiality of user-data without the need to set any further security systems, and enables users to easily integrate the CRREM tool into their existing structures and save their evaluations on local systems. Currently, as CRREM and GRESB have already aligned their input sheets, we are working with GRESB to integrate CRREM into their annual assessment, making the tool even more user-friendly and stranding risks more prominent within the commercial real estate sector. 5. Functional implementation of the “Carbon Risk Real Estate Monitor” tool The CRREM tool allows for up to 250 properties throughout all EU-27 countries (surplus UK) at the moment and is provided for downloading without charge via www.crrem.eu/tool, where users can choose between an empty .xlsx ﬁle and a preﬁlled version, providing some example properties and assessments. Updates, whenever necessary, will also be provided via the respective website. The tool comes with some explanatory remarks on the input sheet and additionally, some video tutorials showing the main functions of the tool, as well as a comprehensive user-guide (“CRREM Risk Assessment Reference Guide”). Additionally, users are invited to ask questions or give feedback on crrem.eu. When opening the tool, the user will see eight different tabs (“Start”, “Targets”, “Input”, “Asset”, “Portfolio”, “Settings”, “Unit Converter” and “Back-end”), each clearly named and accompanied by an explanation following the easy-to-use approach. By clicking on the JERER targets sheet, the tool displays the 1.5°/2°C decarbonisation and energy-reduction pathways 13,3 derived in Section 4.1, and users can choose between different countries and types of use. The input sheet (Figure 5) consists of six sections, asking the user to enter the property- speciﬁc data. Accordingly, each row refers to an individual property and each column represents a speciﬁc input variable. Data can either be entered as text, numbers or drop- down selection, depending on the variable considered. Additional validation checks are carried out in the background, informing the user if entered data is invalid (e.g. non-existent ZIP code). Of more than 50 indicators available, the minimum number of inputs necessary to start the assessment varies between 10 and 20. To ensure the correct allocation, each asset receives an individual asset-identiﬁer (ID) starting with “1” to “X”. Moreover, the user can choose whether or not the property should be included in further assessments by selecting exclude/include. The user is ﬁrst asked to enter the general information relating to the building, such as the asset name, reporting year, Gross Asset Value (GAV), as well as information regarding the reporting year (starting date and length). The analysis is currently available for the reporting years 2018 and 2019. Later, it is necessary to account for uneven reporting periods and varying energy-consumption data. Additionally, the user can allocate the property to a certain entity/portfolio. The second category is for building characteristics like location (incl. ZIP code), property type and – if selected, mixed-use as property type – the ﬂoor area shares of different property types, whether the asset is equipped with air conditioning and – of course – the asset size in terms of total gross internal ﬂoor area (IPMS 2 ), as well as (potential) vacant space. Depending on the type of use, the asset is projected against different decarbonisation pathways. Entering a ZIP code is optional, but improves the assessment accuracy, as further calculations are not aggregated to country-level, but to a more regional-level (NUTS3). CRREM differentiates between the following types of use: ofﬁce, retail high street, retail shopping centre, retail warehouse, industrial/distribution warehouse, hotel, healthcare, lodging/leisure and recreation as well as mixed use. For mixed-use properties, a decarbonisation pathway based on the ﬂoor area shares is calculated. After entering the building characteristics, the user is asked in a third step to provide energy-consumption data for the whole building. The tool separates between grid electricity, natural gas, fuel oil, district heating as well as district cooling and offers two additional ﬁelds for “other energy consumption”, where the user can select (drop-down) between biogas, wood chips, wood pellets, coal, landﬁll gas and liquiﬁed petroleum gas (LPGs). Each amount of energy used has to be provided in kWh. Additionally, the user needs to enter the data coverage and maximum coverage (i.e. if the given consumption data is available and applicable for the complete ﬂoor area or only parts of it, e.g. natural gas consumption only applicable for 8,000 of 10,000 m ). As the tool is not only capable of dealing with emissions originating from energy consumption, but also accounts for fugitive emissions/refrigerant losses, the user is asked in a fourth step to provide data on the type of gas and amount of leakage (if applicable). The tool offers a total of 44 refrigerant gases, each with a speciﬁc global warming potential (expressed in carbon dioxide equivalents). The ﬁfth section focusses on renewable energy, especially, but not limited to electricity, both produced on- and off-site. The tool then differentiates between energy consumed on-site and exported. Generally, to avoid double-counting, off-site renewables do not reduce the carbon risk of individual buildings; however, only renewable electricity purchased directly Carbon risk real estate monitor Figure 5. CRREM Input sheet General informaon Building characteriscs Energy consumpon Asset Reporng Gross Asset Value Air Whole building energy con Inclusion Asset Name Reporng period Enty Locaon Property type Asset size ID year (GAV) condioning Combined energy consumpon of Commo Energy used by tenants and base building services to leable/leasable and common spaces. This should include all energy supplied refurbishment measur Total gross Average annual Grid Electricity Natural gas Fuel oil District heang [steam] Starng month Months of data Country City Zip Code Address internal area vacant area (IMPS 2) Us a ge Da ta Ma xi mum Usage Data Ma xi mum Usage Data Ma xi mum Us a ge Set us er- Da ta Ma xi mum Covera ge Covera ge Covera ge Covera ge Covera ge Covera ge deﬁ ned Covera ge Covera ge Oponal (for emi ssi on Oponal (required for Oponal (only to Oponal (for Oponal (only to Pre- further fa ctor Mandatory calculang certain risk Mandatory Mandatory Mandatory be displayed in improved be displayed in Mandatory Oponal Mandatory Mandatory ﬁlled possibilies of Mandatory Mandatory Ma nda tory i f Ma nda tory i f Mandatory Ma nda tory i f Mandatory Ma nda tory indicators) results) accuracy) results) aggregaon) if us a ge ≠ 0 i f us a ge ≠ 0 us a ge ≠ 0 us a ge ≠ 0 if us a ge ≠ 0 us a ge ≠ 0 if us a ge ≠ 0 if us a ge ≠ 0 [kWh] [m²] [m²] [kWh] [m²] [m²] [kWh] [m²] [m²] [kWh] Hype rl i nk [m²] [m²] Dropdown Text Year [€] Drop-down Number of Months Text Drop-down Text Text/Numbers Text Type of use Drop-down [m²] [m²] EL.GRID EL.DC EL.MC NG.CON NG.DC NG.MC OL.CON OL.DC OL.MC DH.CON DH.DC DH.MC ID INC NAME AS.YR GAV AS.MON AS.LENG ENT COUN CITY ZIP Address AS.TY AC.YN TO.FL BSR_OC.AN Jos ef-Stei nba cher- 1 Incl ude Stei nba ch Tower 2018 2 .000.000 Ja nua ry 10 Aus tri a Wörgl 6300 Oﬃ ce 6.000 300 150. 000 6. 6. 000 000 340. 000 5. 500 6. 10. 000 000 Sengs 5. 500 6. 000 Stra ße 1 Andreas -Hofer- 2 Include Li nden Pa leis 2018 6 .000.000 Ja nua ry 12 Fund 2 Aus tri a Kufs tei n 6330 Mi xed Us e 1.000 0 30.000 1 0.764 10.764 70. 000 10. 764 10.764 10. 000 Sengs 10.764 10.764 Stra ße 9 3 Incl ude Smal l e Ka naal 2018 4. 750.000 Ja nuary 12 Fund 2 Netherl ands Ams terda m 2514 Oﬃ ce 1.500 0 120. 000 1. 000 1. 000 55. 000 700 9 00 10. 000 Sengs 75 1 00 Fugive emissions Renewable energy Retroﬁt acons Oﬀ-site renewable electricity Other on-site renewable On-site renewable consumpon Refrigerant losses / Fugive emissions Generated oﬀ-site and consumed on- energy source (heatpump, Retroﬁt acon 1 electricity (PV, wind) mmon Areas + Tenant Space site solar thermal) plied to the building for the operaon of the building and the tenant space except from energy consumed as part of Whole building (Can only be reported at whole building) Ge ne ra l l y, oﬀ-s i te re ne wa bl e s do not cons tute a easures. Generated Generated Same reporng period as energy consumpon data qua l i ty cha ra cte ri s c re duci ng ca rbon ri s k of Generated Generated on- and i ndi vi dua l bui l di ngs . Onl y re ne wa bl e e l e ctri ci ty and on-site and site and purcha s e d di re ctl y from from a ge ne ra tor / re ta i l e r consumed consumed exported exported through a powe r purcha s i ng a gre e me nt or contra ct District cooling [chilled water] Other energy consumpon type 1 Other energy consumpon type 2 Gas 1 Gas 2 on-site on-site Achieved ca n be a cknowl e dge d unde r s tri ct condi ons . reducon of Embodied energy m Us a ge Set us er- Da ta Ma xi mum Type Usage Data Ma xi mum Type Us a ge Da ta Ma xi mum Type of gas Amount of leakage Type of gas Amount of leakage carbon ge deﬁ ned Covera ge Covera ge Covera ge Covera ge Covera ge Covera ge consumpon related to emi ssi on [%] - Leave Mandatory if amount Mandatory if amount Reporting Em is s ion factor if retroﬁt acon fa ctor Amount Amount Amount Amount Amount Year Investment blank to apply m arket-bas ed ory Ma nda tory Ma nda tory Ma nda tory i f Ma nda tory Ma nda tory Ma nda tory Ma nda tory Ma nda tory of leakage ≠ 0 of leakage ≠ 0 method default values ≠ 0 if us a ge ≠ 0 if us a ge ≠ 0 us a ge ≠ 0 if us a ge ≠ 0 if us a ge ≠ 0 if us a ge ≠ 0 if us a ge ≠ 0 if us a ge ≠ 0 [kWh] Hype rl i nk [m²] [m²] Drop-down [kWh] [m²] [m²] Drop-down [kWh] [m²] [m²] Drop-down [kg] Drop-down [kg] [kWh] [kWh] [kWh] Drop-down [kgCO e/kWh] [kWh] [kWh] [yyyy] [€] [%] [kg] DC.CON DC.DC DC.MC OT1.TY OT1.CON OT1.DC OT1.MC OT2.TY OT2.CON OT2.DC OT2.MC GHG.Leak1.Type GHG.Leak1.Amount GHG.Leak2.Type GHG.Leak2.Amount RF1.YR RF1.EUR RF1.PC RF1.EC Loca on- 2.000 Sengs 5. 000 5 .000 Bi oga s 20 .000 5 .000 5 .000 Bi oga s 1.000 5.000 5.000 Ca rbon di oxi de (CO2) 10 Metha ne (CH4) 10 - - bas ed - - 2024 350. 000 € 50% 400. 000 approach Loca on- 0 Sengs Ca rbon di oxi de (CO2) 20 Metha ne (CH4) 20 - 1 00 bas ed approach Loca on- Wood 0 Sengs 1. 000 700 9 00 Ca rbon di oxi de (CO2) 30 Metha ne (CH4) 30 100 - 0 bas ed pel l ets approach Source: CRREM General informaon Energy consumpon Fugive emissions Building characteriscs Renewable energy from a generator or retailer through a power purchasing agreement or contract has an JERER impact on reducing the carbon emissions. For the scenario of renewable electricity generated 13,3 but consumed on-site, the user can choose between two reporting methods: either using the location-based or market-based approach. Location-based emission factors are based on the average emission intensities of the electricity grid (national grid-averages), whereas the marked-based approach reﬂects the GHG emissions based on emissions by the generators from which the entity purchases electricity. Thus, either the national average or provider- speciﬁc emission factors will be considered in further calculations. The CRREM energy- reduction pathways refer to the ﬁnal energy (actual energy from e.g. utility bills), differing from primary energy which indicates how much energy is needed, for example, by burning fossil fuels. The net energy demand of an asset is considered to calculate the reduction pathway, meaning that energy imports and exports is not identical to the energy consumption of the property. Two different approaches are available with regard to the net energy demand relating to parameters like procurement, consumption, generation and export, as illustrated in Figure 6. Finally, section six enables the user to provide information on planned retroﬁt measures: year of retroﬁt, amount of investment, achieved reduction of energy consumption (default values), as well as the embodied carbon related to the retroﬁt action. One core feature of the CRREM tool is the assessment of retroﬁt actions and their effect on the GHG emissions of a property, so that the user can simulate “virtual” retroﬁt decisions. Details will be outlined in Section 6. Information on the purpose of the different variables can be found in the CRREM Risk Assessment Reference Guide (CRREM, Carbon Risk Real Estate Monitor, 2020a). Additionally, to account for different units, the tool comes with a unit converter enabling its users to convert various energy, weight/mass, and volume metrics directly within the tool. 6. Risk assessment results After entering all necessary data, the tool calculates each asset’s baseline emissions, as well as the climate and grid-corrected asset performance over time (in terms of GHG intensity per square metre [kgCO e/m /a]). The intensity of this particular asset is mapped against the CRREM-derived building-type and location-speciﬁc decarbonisation pathway. Net present costs of excess emissions subtracted from the net present value of emissions below the target is referred to as the climate value at risk. If the emissions exceed the decarbonisation pathway at some point, the tool will display the year of stranding and calculate the excess emissions. To avoid building-speciﬁc bias Figure 6. Deﬁnition of net energy demand regarding the emission data caused by vacant areas, as well as partial or incomplete Carbon risk reporting periods, the CRREM tool will carry out normalisation calculations. real estate monitor 6.1 Stranding risk analysis on asset level The results for single assets are displayed in a stranding diagram (Figure 7) in the asset sheet. Users can use the drop-down list to switch between different assets (based on the asset IDs) and select either the 1.5° or 2°C target as a basis for the stranding-risk assessment. Next to the stranding diagram, the user can once again see the relevant GHG intensity targets for the selected building. Any subsequent emission above the permissible values of the selected pathway (so-called “excess emissions”) are used as one of the risk indicators. The economic obsolescence is associated with the stranding date; the higher the excess emissions, the greater the probability of economic obsolescence. The CRREM follows the approach that properties with low energy efﬁciency and correspondingly high carbon emissions will also face decreased marketability. As mentioned above, the tool further enables simulating retroﬁt measures (see Section 5 for the required input data). If valid data is entered, the tool provides an additional graph displaying the carbon intensity with and without retroﬁt measures (Figure 8), visualising the new point of stranding. If the building is underperforming, only appropriate retroﬁt measures can reduce the GHG emissions, ensuring that the building will meet future emission ceilings. However, the potential effects of climate change with regard to future so- called heating- and cooling-degree days (HDD/CDD) and the electricity gird decarbonisation are per se independent of any retroﬁt measurement. The tool enables the input of one future retroﬁt scenario, displaying the analysis of emission budget depletion, economic and ecological payback. Besides asset underperformance, the strategic timing of retroﬁt actions should also be subject to the refurbishment cycle (exploitation of possible synergy effects), availability and the timing of future sales (if intended). Additionally, the CRREM analysis offers an estimation of future energy consumption (in relation to the property area) and the costs needed to cover energy demand (electricity and fuels), based on estimates of the future heating and cooling requirements of the property (Figure 9). An estimate of the investments required to achieve the decarbonisation targets in energy-efﬁciency measures round off the analysis at the property level. If the excess emissions are multiplied by a carbon price (e/kgCO e), this results in increasing costs due the growing decarbonisation requirements, enabling estimates of imminent ﬁnancial damage. A further risk indicator based on the GAV is the calculation of the net present value (NPV) of these future cash ﬂows (so-called “Carbon Value at Risk”; CVaR). The CVaR enables a comparison of the stranding risks of multiple assets. 6.2 Stranding risk analysis at portfolio level Besides analysing the carbon performance of single assets, the tool also enables an aggregated (graphical and tabular) view of the above-mentioned risk ratios including CVaR and all stranding events of each asset over time. These results are displayed as the share of stranded assets diagram (see Figure 10) in the portfolio sheet. The calculation of shares can either be based on GAV, gross ﬂoor area or simply the number of buildings. Moreover, the user can ﬁlter the portfolio assessment in terms of country, property type, entity and assessment year against the chosen climate target. On an optional basis, the effect of a sale of individual properties at a certain point in time can also be analysed. The CRREM tool portfolio assessment accounts for this fact by providing users with the option to select the year in which they want to sell the asset. Subsequently, the selected assets will not be taken into further consideration. JERER 13,3 Figure 7. Asset-level stranding analysis against the 1.5°C decarbonisation target Carbon risk real estate monitor Figure 8. Asset-level stranding analysis with planned retroﬁts JERER 13,3 Figure 9. Cost of excess emissions analysis Further analysis includes the average portfolio GHG- and energy-intensity benchmarked against the 1.5°C and 2°C decarbonisation targets (Figure 11)with and without retroﬁt scenarios, so as to illustrate the scale of impact of retroﬁt investments on the portfolio’s exposure to stranding. The aggregated costs of excess emissions of all assets within a selected portfolio are displayed. Lastly, a portfolio summary is provided in a tabular format including an overview of the stranding year, discounted costs of excess emissions, cumulated excess emissions and the respective emission budget until 6.3 User-speciﬁc assumptions In addition, the CRREM tool enables the input of one’s own assumptions, including the input of user-deﬁned decarbonisation pathways which enables the user to enter individual decarbonisation pathways and emission factors for each asset. Default values can be altered to allow for one’s own assumptions and more user-speciﬁc input. The user has the option to change the asset-level settings to normalise consumption data to 100% occupancy and to normalise the weather by normalising current heating- and cooling-degree days. Further climate-change projection can be selected, changing future climate projections and affecting the future heating and cooling demand. The Representative Concentration Pathway (RCP) is a GHG concentration trajectory adopted by the Intergovernmental Panel on Climate Change (IPCC). The RCP8.5 projects a steep incline in GHG concentration of over 1200 ppm of CO - equivalents, while the RCP4.5 estimates a moderate inclusion of 650 ppm CO -equivalents 2 Carbon risk real estate monitor Figure 10. CRREM portfolio analysis; development of the proportion of stranded assets based on GAV JERER 13,3 Figure 11. Portfolio alignment with the Paris targets until 2100 (Moss et al.,2010). Additional assumptions include the possibility of setting one’s Carbon risk own (constant) emission factors for electricity, district heating and district cooling. Lastly, real estate the user can deﬁne and enter his own energy and carbon prices, the annual rate of change as monitor well as altering the discount rate for valuing future expenditure and saving (default: 2%). Furthermore, the tool enables advanced users to set user-deﬁned electricity emission factors for each year, user-deﬁned energy prices for electricity, gas heating and cooling for each year, as well as user-deﬁned decarbonisation pathways (more or less restrictive). However, we recommend caution in the use of these options, as especially less restrictive decarbonisation pathways might severely bias the risk assessment. Following the idea of a transparent tool, all calculations are carried out in the back-end sheet, enabling users to easily understand the structure. All entered property data and the corresponding results can be saved as a regular excel (.xlsx) ﬁle. 7. Conclusion and outlook Ongoing climate change and the accompanying regulatory efforts jeopardise the business viability of real estate companies. However, existing research focusses mainly on the upsides of climate change in terms of higher rents, sales prices or lower operating costs instead of downside risks. When downside risks are in fact assessed, the predominant focus has been – up to now – especially on natural hazards and extreme weather events. By contrast, decarbonisation in terms of the 1.5°/2°C targets, carbon assessment and stranding risks have so far not received any signiﬁcant research attention, leading to a persistent lack of information availability and transparency, as well as uncertainty in the real estate sector. Existing tools for the assessment of carbon intensity focus solely on current emissions instead of providing speciﬁc decarbonisation pathways, or are methodologically unsound and/or limited in scope, as they, for example, consider few or no different assets classes and often do not distinguish between different geographical locations. The examined approaches did not cover all relevant emission sources, only accounting for selected building features, are partially limited to one asset instead of providing the possibility to assess whole portfolios and have not achieved broad market acceptance. With CRREM, we have not only deﬁned principles for a carbon assessment instrument but developed a suitable tool for the assessment of carbon risks. To the best of our knowledge, CRREM is the ﬁrst and only tool which provides location- and building-type speciﬁc science-based decarbonisation pathways, thereby enabling institutional investors and other stakeholders to start assessing their properties and portfolios in terms of carbon intensity and stranding risks. As a result, CRREM overcomes the lack of information and transparency in the commercial real estate industry, making carbon risks measurable and thereby triggering retroﬁt activities. To ensure a broad market acceptance and high-quality standards, a broad partnership consisting of various stakeholders including institutional investors, asset managers and other corporate partners, as well as industry bodies and scientists, accompanied the development of the tool and tested the pilot version of CRREM under real conditions. Numerous large international real estate investors have tested the tool, which resulted in over four million square metres in lettable space being analysed during the pilot phase. Up to now, the focus of CRREM has been limited to the commercial real estate sector in countries of the European Union only, but by deriving further decarbonisation pathways for non-European countries (incl. the United States, Japan, Singapore and many other important investment locations), a ﬁrst step towards a global roll-out has already been made. JERER Furthermore, additional pathways for residential properties have been derived, enabling a 13,3 broader use of the tool. The implications for different stakeholders are as follows. Both for institutional investors and asset managers as well as other stakeholders, the tool provides a suitable basis for investment decision-making. The tool accounts for different locations as well as building- and usage-type speciﬁc features. All in all, CRREM provides a valuable basis for assessing carbon intensity and the assessment of stranding risks, providing appropriate guidance for the decarbonisation of the commercial real estate industry. Acknowledgements The authors are grateful to members of the CRREM European Investor Committee (CRREM EIC) for their scientiﬁc support and advice. They also express their appreciation to APG Asset Management (Derk Welling, Senior Responsible Investment and Governance Specialist), PGGM (Mathieu Elshout, Senior Director, Private Real Estate Europe and Stan Bertram, Junior Investment Manager, Private Real Estate), Norges Bank Investment Management (Christopher Wright, Sustainability Manager, Real Asset Risk), GPIF (Hiroyuki Yabe, Director, Private Market Investment Department) and Ivanhoé Cambridge (Stéphane Villemain, Vice President, Corporate Social Responsibility and Rob Simpson, Director Sustainability) for supporting the derivation of further decarbonisation targets for non-EU countries, which can be downloaded via crrem.org/pathways/. This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 785058. The authors, as well as the consortium as a whole much appreciate this support. The CRREM consortium consists of ﬁve well-known institutions from various European countries, all with many years of experience in the ﬁeld of energy efﬁciency and carbon research: IIÖ Institute for Real Estate economics (Austria, project coordinator), University of Alicante (Spain), Ulster University (United Kingdom), GRESB (Netherlands), and Tilburg University’s TIAS Business School (Netherlands). Notes 1. See Bienert et al. (2016) for a meta-study of more than 70 green-pay oﬀ studies worldwide. 2. Ongoing climate change might lead to a shift in HDD and CDD, leading to a shift in heating and cooling demand at various locations. We apply the climate model data of Spinoni et al. (2018) to the future development of heating- and cooling-degree days (HDD/CDD) (Day, 2006), to cover the eﬀect of climate change on the development of future heating- and cooling-degree days (see upper dashed line in Figure 1). 3. 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Journal of European Real Estate Research – Emerald Publishing
Published: Jul 24, 2020
Keywords: Risk management; Commercial real estate; Science-based targets; Decarbonisation; Downscaling; Carbon risk; Stranding risk; Excess emissions
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