**Environmental and Economic Evaluations of Building Energy Retrofits**

Editors

**Cynthia Hou Joseph H.K. Lai**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Cynthia Hou The Hong Kong Polytechnic University China

Joseph H.K. Lai The Hong Kong Polytechnic University China

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## **Contents**

## **About the Editors**

## **Cynthia Hou**

Cynthia Hou has been engaged in research on facilities management in both the private and public sectors. Her areas of research focus include post-occupancy evaluation, workplace management, strategic facilities management, and heritage adaptive reuse and revitalisation. Recently, she has leveraged smart technologies in her research by investigating smart hotels, smart heritage facilities management, and virtual reality (VR) technologies applications in built environment education. She seeks to investigate efficient and sustainable built environment management strategies from a facilities management perspective. She is also keen on adopting digitalisation approaches in her research, with the aim of delineating the dynamics among people, facilities, and space in the built environment and exploring data-driven solutions for facilities management problems in society. Cynthia is a member of the Royal Institution of Chartered Surveyors and the programme leader for the Master Programme in Facilities Management, The Hong Kong Polytechnic University

## **Joseph H.K. Lai**

With a first-class honours degree in building services engineering, a master's degree in environmental management, a PhD in facility management (FM) and over a decade of professional practice, Dr Joseph Lai combines professional and academic roles as a scholar in FM teaching and research. Dr Lai is Associate Head of the Department of Building Environment and Energy Engineering, Faculty of Construction and Environment, The Hong Kong Polytechnic University. The honours Dr Lai has received include the Teaching Excellence Award, Faculty Teaching Excellence Award, Dean's Award for Outstanding Achievement in Academic Programme Development, and Distinguished Educator Award of the International Facility Management Association. Dr Lai is Co-Editor of the Facilities journal and Associate Editor of the *Smart and Sustainable Built Environment* journal.

## *Article* **Transaction Cost and Agency Perspectives on Eco-Certification of Existing Buildings: A Study of Hong Kong**

**Yung Yau 1, Huiying (Cynthia) Hou 2, Ka Chi Yip 3 and Queena Kun Qian 4,\***

**Abstract:** Eco-certification schemes are usually launched with various incentives provided by local governments to facilitate green building development and building energy retrofits. A number of barriers to building energy retrofitting have been identified in previous literature, while the barriers to the eco-certification of existing buildings are under-researched. Drawing on a set of building data retrievable from the BEAM Society and other sources, we carried out an analysis and found the building energy retrofitting, as well as the certification process, were unwelcomed in multi-owned residential buildings. The identified shortfall is put forward from the perspectives of transaction cost theory and agency theory. The findings reveal that high transaction costs incurred during negotiations and coordination among a large number of co-owners within a typical apartment building can outweigh the benefits of retrofitting and eco-certification. Besides, the remuneration structure of third-party property management agents discourages agents from facilitating co-owners to initiate retrofitting. This study provides significant implications for policymakers to understand the concerns of building owners and managers over the decisions and the processes of both the building energy retrofits and eco-certification. The problems and barriers unveiled in this study will facilitate the refining of current energy efficiency policies and related incentives designs.

**Keywords:** building energy performance; building energy retrofits; green building certification; transaction costs; agency theory; incentives

## **1. Introduction**

In 2019, building construction and operations accounted for 35% of final energy consumption and 38% of energy (and process-related) emissions [1]. The latest global energy consumption data suggest that building energy use remains a significant proportion of overall energy demand. The percentages of building energy consumption against total final energy consumption in different areas are shown as follows: 57% in Africa, 40% in Europe, 26% in ASEAN, China, and India, and 24% in Central and South Africa [1,2]. Many governments across the world have made significant efforts in promoting energy efficiency in their built environments, providing various initiatives to drive building owners or operators to retrofit their existing buildings. The United States has launched and planned 203 energy-efficiency related policies for the building sector, followed by Australia and Canada, with 94 energy efficiency policies, respectively [3]. China has launched and planned 236 energy efficiency policies, with 59 being relevant to the building sector, varying from regulatory instruments, building codes and standards, minimum energy performance standards, economic instruments, strategic planning, etc., to fiscal/financial incentives.

To promote energy efficiency initiatives in Hong Kong, where buildings account for 90% of electricity consumption, the Hong Kong government has adopted both statutory

**Citation:** Yau, Y.; Hou, H.; Yip, K.C.; Qian, Q.K. Transaction Cost and Agency Perspectives on Eco-Certification of Existing Buildings: A Study of Hong Kong. *Energies* **2021**, *14*, 6375. https:// doi.org/10.3390/en14196375

Academic Editor: Antonio Gagliano

Received: 1 September 2021 Accepted: 27 September 2021 Published: 5 October 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

4 Faculty of Architecture and the Built Environment, Delft University of Technology,

and non-statutory approaches to enhance the city's overall building energy performance. In Hong Kong, Leadership in Energy and Environmental Design (LEED) and BEAM Plus (a green building rating tool managed by the Hong Kong Green Building Council) are two major environmental building rating and certification schemes. They have been well received by the construction industry in Hong Kong. In the past decade, the two voluntary schemes have significantly facilitated green building development and fostered building energy reduction in Hong Kong. The two schemes include a set of rating systems for the design, construction, operation, and maintenance of buildings and neighbourhoods, based on which, the building owners have their buildings/projects certified to obtain eco-certification. Among the 42,000 existing buildings in Hong Kong, 1.5% (approximately 630 buildings) have accredited or registered to the BEAM plus certification system, and 0.34% (approximately 142.8 buildings) have accredited or registered to the LEED certification system [4]. In recent years, two main concerns for the Hong Kong government—to achieve energy reduction targets of 40% in the built environment by 2025—involved enabling existing buildings toward energy retrofitting, and obtaining certification under energy-efficiency related schemes [5]. In 2019, BEAM Plus New Buildings (BEAM Plus NB) and BEAM Plus Existing Buildings (BEAM Plus EB) were launched to foster a sustainable built environment in Hong Kong.

It is easier to have an aged building eco-certified if it has undergone energy retrofitting. However, in reality, the reasoning and decision-making processes for existing building energy retrofitting and eco-certification can be complicated. For example, the barriers to eco-certification of existing buildings would affect the decisions of building energy retrofits, especially when the building owners' decisions are motivated by the incentives brought by the eco-certification scheme [6]. In this light, investigating the patterns of eco-certification of existing buildings would help deepen the understanding of the barriers toward building energy retrofits. In this study, an inductive approach is adopted to examine the characteristics of the existing buildings that are eco-certified. The factors that hinder implementing the building energy retrofits and the eco-certification are identified and discussed from a transaction cost theory and an agency theory perspective. The findings of this study will help policymakers understand the concerns and behaviours of building owners regarding the process of eco-certification application, enable building owners and operators to evaluate the costs occurred by the barriers, and make rational decisions to overcome possible barriers in the future.

The paper is organised as follows: Section 2 provides a literature review on the barriers to building energy retrofits and the eco-certification of existing buildings. Section 3 introduces the methodology of this study followed by a detailed presentation of the findings in Section 4. Section 5 elaborates the discussion based on the findings and Section 6 presents the conclusion of this study.

## **2. Context of the Research**

In practice, local governments support eco-certification by launching various incentives in both statutory and non-statutory forms (e.g., policy incentives, tax incentives, funding assistance, etc.) [6]. Furthermore, eco-certification organisations also integrate incentives into the design of the eco-certification schemes and their certification processes, such as reducing the cost for volume certification, creating different application pathways, etc. The benefits of these incentives are attractive to developers and building owners and, to a certain extent, serve as a huge motivation for them to apply for eco-certification for new projects.

The barriers to green building development have been profoundly discussed in numerous studies. Darko and Chan [7] provided a comprehensive review of the barriers to green building adoption and identified 37 barriers in the literature. Arguably, these barriers also hinder the eco-certification of new buildings. Perspectives derived from the transaction cost theory (TC) [8,9] and cost–benefit analysis [10–12] have been used to analyse the developers' behaviour and the development process of green building development. Moreover, eco-certification of new dwellings may not be considered because homebuyers may pay little attention to the eco-certification or energy labels in their home purchase decisions [13,14].

Yet, the extant literature does not distinguish the barriers to green building development and barriers, to obtain eco-certification for buildings. In other words, discussion on the barriers to eco-certification of new buildings is usually mixed, regarding the barriers to green building development. Even worse, there has been a dearth of literature on the barriers to eco-certification and energy retrofits of existing buildings. The factors that incentivise or dis-incentivise existing buildings' eco-certification and energy retrofits could be different from those associated with new buildings. Therefore, it is worthwhile to study the factors that shape decision making for eco-certification and energy retrofits in our existing building stock.

The focus of this study is to investigate the possible barriers to building energy retrofits through examining the issues that affect the eco-certification of existing buildings based on the database of eco-certification of existing buildings in Hong Kong. Thus, the target of the investigation is the management of existing buildings, such as the decision on whether to implement building energy retrofits and apply for eco-certification, the selection of the certification pathway, etc.

## *2.1. Barriers to Building Energy Retrofits and Eco-Certification of Existing Building*

Building energy retrofitting works generally involve "replacements, modifications, and refurbishments of existing buildings to enhance the energy efficiency, conservation, and savings" [15]; the minimization of energy consumption and the maximization of economic benefits are the two prime objectives of retrofitting. Building control improvement and building component implementation are two major energy-retrofitting strategies to increase energy efficiency and reduce the energy demand of the building [16]. The process of building energy retrofits involves multiple stakeholders from different professional backgrounds and with different intentions towards the retrofitting decisions. Among the involved stakeholders, building owners play an important role in building energy retrofitting and eco-certification decision-makings. A number of barriers regarding the stakeholders' perceptions towards building energy retrofits, as well as their expected outcomes, are identified in the literature. For example, building owners may have an aversion to energy efficiency refurbishment measures because of the lack of interest in energy efficiency issues, financial means, long-term perspectives, and trust towards contractors [17]. As far as municipalities are concerned, factors, such as unawareness about the energy problem, difficulties with goal setting and data collection, and lack of expertise in the municipalities to analyse the data and develop an effective plan, hinder government-led energy retrofit projects [18].

Hong et al. [19] studied the commercial building energy retrofitting projects in China and suggested that a lack of expertise and resources to identify and evaluate cost-effective energy retrofit strategies are major barriers for owners when it comes to pursuing energy retrofitting. Hou et al. [20] identified a number of issues that would decrease building owners' willingness to retrofit their buildings, including unclear stakeholder obligations, difficulties in coordinating multiple parties, and complexity of retrofit implementation. Aside from individual research projects by academic scholars, international associations also put effort into identifying the barriers to building energy retrofitting. Building Performance Institute Europe identified four barriers to building energy retrofits, categorizing them into four categories: (1) financial; (2) institutional and administrative; (3) awareness, advice, and skills; and (4) separation of expenditure and benefit [21]. Climate Policy Initiative also identified four barriers to building energy retrofits, namely: (1) embryonic markets; (2) lack of information; (3) misaligned financial incentives; and (4) undervaluing energy efficiency [22].

As a significant number of existing buildings are aging and facing an urgent need to upgrade their operational, economic, and environmental performance worldwide, the pursuit of green certification for existing buildings has become an inevitable trend [23]. Aktas and Ozorhon [24] only focused on the green building certification process and identified three major barriers to existing the eco-certification process of buildings in developing countries—unavailability of approved materials, poor design of buildings, and difficulties with the documentation process. One of the findings from their study was that the building owners do not perceive the cost as a barrier to 'green' their existing buildings. The possible reason is that the building owners (owners of commercial buildings) see the certification as an opportunity to enhance their corporate image. Thus, they are more flexible with the budget on green implementation of existing buildings.

## *2.2. Building Energy Retrofits and Eco-Certification in Hong Kong*

## 2.2.1. Statutory Regulations and Policy Incentives for Building Energy Retrofits

In Hong Kong, the Building Energy Efficiency Ordinance (BEEO) (Cap. 610) was formulated to enforce certain prescribed types of buildings to comply with building energy codes (BECs) and/or the energy audit code (EAC). In addition, the Hong Kong Energy Efficiency Registration Scheme for Buildings (HKEERSB) was introduced to recognise buildings that outperform the statutory requirements under the BEEO. The HKEERSB was officially launched in 1998 in order to promote the adoption of the BECs by providing the certification to a building complying with one or more of the BECs [25].

In 2018, the Hong Kong government launched a tax incentive scheme to further encourage building owners to pursue the application of HKEERSB: as long as the building owners have their buildings certified by BEAM Plus (managed by the Hong Kong Green Building Council) or other internationally recognised building environmental assessment systems, such as LEED, they are eligible to apply for relevant tax deductions [26,27]. Building owners and building managers are familiar with the BEEO. The newly launched tax incentives have drawn the attention of policymakers to the barriers of building energy retrofits and the eco-certification process for existing buildings in Hong Kong.

## 2.2.2. Background of the BEAM Plus

Building Environmental Assessment Method (BEAM) Plus is the prevailing rating tool for green buildings in Hong Kong. BEAM Plus, conceived in 1996 as a voluntary private sector initiative, has developed into an internationally recognised green building rating tools for new buildings (NB), existing buildings (EB), interiors (BI), for shops, office, retails, and neighbourhood (ND) [28]. To achieve the target set out in the Energy Saving Plan by 2025, the Hong Kong Green Building Council (HKGBC) issued the new version of BEAM Plus Existing Building (BEAM Plus EB) V2.0 in 2016 [5,29]. It includes the new assessment framework with two certification pathways: a comprehensive scheme and a selective scheme. In order to have the building certified under the comprehensive scheme of the BEAM Plus EB, the building performance shall be assessed under all seven aspects, including management, energy use, indoor environment, water use, materials and waste aspects, site aspects, and innovation and additions. The applicant can also choose to have the building performance assessed under one or more specific aspects through the selective scheme pathway. Based on the assessment scores by BEAM professionals, the buildings are awarded certain ratings (e.g., platinum, gold, silver, and bronze) that reflect the actual performance of the building. The four-level rating system applies to both a comprehensive scheme and a selective scheme [29,30].

In order to promote and facilitate the certification of BEAM Plus EB, a volume certification mechanism was introduced to provide a faster and more economical manner for certification application. The applicants can choose to have all buildings or multiple buildings certified by a portfolio assessment mechanism in one go, at a lower cost, through a volume certification approach. This study focuses on the buildings that are certified under the BEAM Plus EB and analyses their background information, including the application mechanism (comprehensive or selective scheme), the certification rating, the building ownership status, the building/estate development information, and the geographical information.

## *2.3. A Transaction Cost (TC) and Agency Perspectives towards Building Energy Retrofits*

Transaction cost (TC) theory has been widely used to analyse the externalities that occur within a firm, in the interaction between a firm and the market, and the process of public policies implementation [31–33]. Transaction costs impact economic performance because high transaction costs can lead to failure of an institutional arrangement [34,35]. Prohibitively high transaction costs tend to inhibit collective actions [36,37]. "Transaction cost" has been discussed and measured in numerous studies on public policies to promote green buildings [7,8,10,38]. It is the main idea of these works that certain types of TCs would occur with different stakeholders in the process of development, certification, and management of the green construction project, and these TCs would undermine the policy's effectiveness and implementation efficiency. It is commonly found that some common barriers exist during the implementation of green building development-related policies, such as information gathering [39,40], internal and external negotiation [39,41,42], innovative technology acceptance, and adoption [43,44]. These studies conceptualised the identified "barriers" with TCs and used TC theory to transform the vague phenomenon into a tangible concept. Qian et al. [8] adopted the transaction cost theory with support of the empirical data from expert interviews to examine the cost and benefits (both actual and hidden ones) in the process of implementing the gross floor area (GFA) concession incentive scheme in Hong Kong. With a similar approach, Fan et al. [9] used the transaction cost theory to measure three dimensions of transaction costs involved in the scheme implementation, namely asset specificity, uncertainty, and frequency. With the theoretical base on transaction cost theory, Fan et al. [11] conducted a case study using empirical data to support the hidden costs and benefits of the same scheme.

Agency theory is often used to explain and resolve disputes over priorities between principals and their agents. The difference in agreement between the principals and agents during the transaction process leads to agency problems. The agency problems generally result from the conflicting goals of the principals and agents, intensified by the information asymmetry [45,46]. The principal–agent dilemma may also stem from moral hazards and adverse selections. A moral hazard occurs when an agent attempts to make a profit on a contract because the principal is unable to observe the agent's behaviour after entering into the contract [47]. On the other hand, uncertainty concerning an agent's characteristics and preferences prior to creating a contract could lead to an adverse selection [48].

In the past decade, agency theory has been increasingly employed to analyse green management strategies in organisations [49–51]. Yet, studies using agency theory in investigating energy efficiency are still rare. Kumbaro ˘glu and Madlener [52] attempted to analyse building energy efficiency problems, focusing on investigating the benefits and conflicts of interests between investors and users based on agency theory. Liang et al. [6] adopted agency theory to explain the agency problems in energy-efficiency retrofits and developed a principal–agent model to map out the problems of two sets of principal–agent relationships: government vs. building owners and building owners vs. tenants. They argued that "incentives" play an important role in the benefit–cost analysis in energy retrofitting decisions. The model illustrates the economic relationships among the three stakeholders (government, building owners, and tenants) under four scenarios. In building energy retrofitting and the eco-certification process, property management companies play the 'agent's' role, implementing the tasks (building energy retrofitting and eco-certification) for the building owners, who play the 'delegator' role, according to the principal–agent relationship. Although both sides in the contractual relationship receive certain incentives from the eco-certification schemes, their interests appear to be imbalanced under certain circumstances. This study aims to identify the possible causes that hinder property management companies from facilitating building owners to implement building energy retrofits and gain eco-certification.

## **3. Research Methodology and Data**

An indicative approach was adopted for this empirical study. Secondary data about eco-certification under BEAM Plus were first collected from various sources. The data were then consolidated and analysed to see if there were any specific patterns regarding ecocertifications of the existing building in Hong Kong. Possible explanations were then tendered to explain the patterns. In the stage of data collection, a list of certified existing buildings was captured on 21 May 2021, from the HKGBC BEAM Pro Project Directory. (https: //www.hkgbc.org.hk/eng/beam-plus/beam-plus-dir-stat/BEAMPlusDirectory.jsp, accessed on 21 May 2021) Only the valid and certified BEAM Pro existing buildings projects available on the online project directory on the captioned date were selected for investigation. In total, the project directory contained 193 existing building projects and 201 valid BEAM Pro existing building certifications. The number of building projects and certifications did not reconcile because a few building projects obtained more than one BEAM Plus certification in BEAM Plus V2.0 (Selective Scheme).

Figure 1 outlines the data collated for desk study with their sources. We principally examined three main categories of information, including (i) Hong Kong BEAM ecocertification figures (certification scheme, project rating, certification year); (ii) background information of each BEAM Plus certified building; and (iii) property governance matters (e.g., ownership status and owner's information). After the data collection, all relevant data were compiled into a project-specific database for further processing. The skeleton of this project-specific database was primarily connected to two local green building databases—HKGBC's BEAM Project Directory and BEAM Society Limited's BEAM Plus Certified Building Database. We also amassed other necessary project data, such as the completion year and the property management company, in diverse sources. Trustworthy databases managed by the government sectors, property consultant agencies, and the local property management professional bodies were utilised. In addition, we reviewed the websites and publications, such as annual reports and environmental, social, and governance (ESG) reports of the property owners and management companies to ensure information accuracy. All information had undergone further validation by cross-checking to ensure data precision.

**Figure 1.** Research design, data retrieving, and analysis process.

To prevent ambiguity, the term "buildings" in the results section refers to "building projects" (the building or a group of buildings) with respect to BEAM Plus certification records.

## **4. Results**

This section will present the analytical results of the eco-certification existing buildings in Hong Kong.

## *4.1. By Certification Schemes and Ratings*

Table 1 shows that only 41 buildings in Hong Kong are completely certified with six sustainable building areas. (This figure aggregates 40 building projects that achieved BEAM Plus comprehensive certification and an NGO headquarter, which gradually achieved all six performance aspects through BEAM Existing Buildings Version 2.0 Selective Scheme.) Approximately 20% of the certifications were recognised through the comprehensive scheme, while more than 80% of BEAM EB certifications were awarded in the selective scheme. Most BEAM-certified existing buildings were rated in the management aspect only. This kind of certification contributed to nearly 90% of the certifications in selective schemes and 71% of all types of certifications (including comprehensive scheme and selective schemes). Although energy use and site aspects were the second and third most prevalent aspects rated in the selective schemes, they respectively contributed to 6.2% and 2.5% of selective certifications across six sustainability fields. Excluding an outlier project where the headquarters of a non-governmental organization (NGO) was certified in all six aspects of the selective scheme, i.e., equivalent to a comprehensive certification, no selective certification was recognised in the area of material and waste aspects or indoor environmental quality. The proliferation of management certifications also skewed the overall distribution of the green EB certification rating, as shown in Table 2.

#### **Table 1.** Breakdowns of certifications by schemes (N = 201).

1 ()—the percentage of participated scheme in respect of all BEAM Plus certifications. 2 ()—the percentage of participated aspect in respect of BEAM EB Version 2.0 Selective Scheme.

In general, over 97% of BEAM existing buildings were awarded a silver/good rating or higher. For the volume certification of the property portfolio submitted by the two major developers (Sun Hung Kai Properties and Link REITs), and their subsidiary property management companies, more than 60% of the certifications are classified into "Final Silver/Good". A small proportion of applications (1.5%) are assigned to a satisfactory classification.

On the other hand, the final ratings of the projects through the two certification pathways are different. There is a significant relationship between the final rating and certification pathway (χ2 = 149.54; *p* < 0.01). If solely considering the final rating by individual certification, it is revealed that around 85% of the certifications are given with

the highest rating (final platinum or excellent). It implies that applicants applying local green building schemes through individual certification may focus on the sustainability dimension qualities (maximum credits achieved). Yet, why do the individually certified have higher ratings? Do project types and building uses also affect BEAM certification methods? Moreover, as it is hypothesised that buildings with single ownership pay fewer transaction costs for green building retrofitting, what are the implications of different ownership statuses on BEAM EB certifications? We analyse property management governance of BEAM-certified buildings by their ownership status, building uses, and owner types.

## *4.2. By Building Ownership Status, Project Types, and Sectors*

The previous section showed that the majority of BEAM-certified existing buildings were assessed only based on the aspect of sustainable building management. It is equally important to examine the ownership status, building type, and sector in unpacking the potential barriers of sustainable building retrofits in Hong Kong. Generally speaking, developers, property owners, and property management companies are imperative in regard to building governance and sustainable building retrofit. Collective actions are necessary for co-owners to initiate building energy retrofits. From the transaction costs and agency perspectives, it is expected that existing buildings in multiple ownerships are less likely to be eco-certified. This is because high transaction costs are usually incurred in the negotiations and coordination among co-owners of multi-owned properties when it comes to initiating certification or improving common areas of buildings. Besides, co-owners in multi-owned properties often need to rely on property management companies in ecocertification or energy retrofit projects. The companies may not act in the best interests of the co-owners.

Table 3 breaks down the certifications by ownership status, project types (or building uses), and owner types. Regarding ownership status, it is found that nearly 72.5% of certified existing buildings in Hong Kong are owned by a single owner (hereafter: singleowned), while the rest (27.5%) are owned by multi-owners (hereafter: multi-owned). Regarding the certification pathways—a difference in terms of ownership status can be seen. The ownership status ratio (i.e., number of certified single-owned buildings to that of certified multi-owned buildings) surged to about nine to one (90% single-owned versus 10% multi-owned) if cases where volume certification was excluded. (Contrasting ownership status outcomes are observed if we separate the samples concerning certification pathways. Regarding ownership status per application type, around 35% of projects via portfolio certifications are multiple-owned (whereas 63.6% are single-owned correspondingly). By contrast, about 11.7% of projects via individual certifications are multiple-owned (and 88.3% of projects are single-owned).) This implies that more single-owned buildings applied the BEAM certification via individual application than via volume application.

By project type, nearly 70% of BEAM-certified existing buildings are commercial properties, while 15% are residential buildings. Industrial buildings, government/institution or community (GIC) buildings, and buildings of other types only account for approximately 8%, 6%, and 2% of the whole sample, respectively. All 15 industrial buildings were certified through volume certification through the selective scheme ("Management" aspect) by a major developer. The figures in Table 3 indicate that the BEAM-certified existing buildings in Hong Kong are predominantly single-owned commercial use. As far as the owner type is concerned, more than 92.7% of the eco-certified projects belong to the private sector, whereas the public organizations or NGOs own less than 10%. There is a significant relationship between the project type and sector (owner type) (χ2 = 168.42; *p* < 0.01). The reason behind such findings is straightforward. The GIC projects predominately belong to the public sector while nearly all commercial, residential, and industrial projects are from the private sector.

**Table 3.** Cross-table showing the breakdowns of certified projects by project types, ownership status, and owner types (N = 193).

1 SO: single ownership; 2 MO: multiple ownership; 3 others included freight forwarding centres, data centres, and technology parks. 4 Some projects count as half (0.5) for a particular property use. For example, a mixed-use project (residential-cum-commercial project) counts as 0.5 for "commercial" and 0.5 for "residential".

> Moreover, a significant relationship is found between the sector (owner type) and certification pathway (χ2 = 34.28; *p* < 0.01). Moreover, projects certified via volume certification have an average score significantly higher than those certified individually (*t*-statistics = 15.08; *p* < 0.01). That means volume certification generally results in less superior eco-labels. Table 4 enumerates the BEAM-certified projects by the project's key owners or property developers. Sun Hung Kai Properties has the greatest number of BEAM-certified existing buildings projects (75 projects in total or 38.8%), followed by Link REIT (50 projects or 25.9%), Swire Properties (13 projects or 6.7%), as well as Nan Fung Group (12 projects or 6.2%). However, 96% of Sun Hung Kai Properties projects were granted through the volume certification pathway under the selective scheme ("management" aspect). Similarly, all projects managed by Link REIT and Nan Fung Group were certified via volume certification under the selective scheme ("management" aspect). For Swire Property, HKSAR Government and Hongkong Land, have the most BEAM-certified existing buildings through the individual certification pathway citywide. For the Swire Property and Hongkong Land, all their certified projects are single-owned Grade A commercial buildings with the highest performance grading (Final Platinum). The records of these buildings were traced against the HK-BEAM system (HK-BEAM certification is the oldest version of BEAM tool). It reveals that all of them were previously certified in either/both HK-BEAM new or/and existing buildings, and nine of them were HK-BEAMaccredited new buildings with the highest rating (Platinum) (certified between 1996 and 2005). In other words, these BEAM-credited existing buildings are either pre-existing green buildings certified by HK-BEAM certification before, or buildings that were managed in a sustainable manner in previous years. Single ownership and engagement of a subsidiary property management agent facilitate the sustainable building retrofitting and eco-certification process.

**Table 4.** Breakdowns of certified projects by developers or property owners (N = 193).

1 Regarding Joint Venture Project or ownership status with complicated situations, this table regards the major developers as (1) the company who carried out BEAM certification as main developers if equally shared; (2) the one with the largest ownership share. 2 This refers to the arithmetic mean of BEAM Plus ratings of all projects in the portfolio of a particular organization, with 1 = final platinum/excellent; 2 = final gold/very good; 3 = final silver/good; and 4 = final bronze/satisfactory. 3 The energy use certification of Sun Hung Kai Centre was obtained through the individual application while the management certification was through volume application. We count this project in both certification pathways.

> We should note that that many applications for certifications were lodged by the property management agents rather than the property owners. Furthermore, there are vigilant affiliations between the owners/developers of the certified buildings and the property management agents managing the buildings. Among the BEAM-certified existing buildings owned by the private sector, 99% (178 out of 179) of the BEAM-certified buildings are developed and managed by companies that belong to the same groups. In many cases of multi-owned private buildings, the property management companies concerned are subsidiaries of the developers. For single-owned buildings, the building owners often dedicate the property management tasks to their own specialised in-house property teams or subsidiary property management companies. Table 5 enlists the building owners (or developers) and property management companies that have close relationships.

**Table 5.** Reciprocal relationships between developers and property management companies.

## *4.3. By the Building/Estate Development Scale*

The development scales of certified projects of residential buildings can be measured with two indicators: (i) the number of residential units; and (ii) the gross floor areas (GFAs) of the projects assessed by BEAM professionals. Two classification methods with equal intervals and Jenk's natural breaks were adopted to estimate the number of residential units, and the estimation was undertaken using the geospatial thematic classification with the aid of the software QGIS 3.

Table 6 shows the breakdowns of BEAM-certified projects for residential uses by the number of residential uses using two different intervals. For the 29 BEAM-certified existing buildings for residential uses, a total of 25,096 residential units were identified, comprising about 0.85% of the total housing stock in Hong Kong (the total number of residential units in Hong Kong as at 2020 was about 2,924,000 [43].) However, most of the residential buildings were certified under the selective scheme with regard to the management aspect, and their certification applications were driven by the property management companies. Among the 29 samples, one project, a senior staff quarters tower for a government-funded university (renovated in 2016), was assessed under the comprehensive scheme, while 4 (out of 29) residential projects were certified via the individual certification pathway.

By Jenk's natural break classification, over one-third of the BEAM-certified projects had less than 265 residential units. Three projects were low-rise luxury terrace-type residences, and another seven were single/twin high-rise towers. Furthermore, one large-scale estate (City One Shatin) was certified via individual certification under the selective scheme ("Management" aspect). In this project, the management company only selected 3 out of 52 blocks in the estate (536 out of 10,642 units or 5.3%) for BEAM Plus certification

application. The possible reason behind this might relate to the collective decision-making issue, which is that the agreement among residents was difficult to seek. This situation may affect the decision-making of green building retrofitting in the estate. This helps explain why no multi-owned large-scale housing estates are awarded or registered for BEAM eco-certification by their individual applications. This phenomenon will be further discussed in Section 5.

**Table 6.** Number of residential units of BEAM-certified projects for residential uses (N = 29).

Unlike their residential counterparts, many non-residential projects often have an open-plan design. It is thus impracticable to compare project scales solely based on the unit numbers. Yet, it is still envisaged that an existing building with a larger GFA is likely to have more owners or tenants than the one with a smaller GFA. Therefore, the number of the owners or tenants involved in a project can be roughly estimated by the assessed GFAs. As indicated in Table 7, about one-third of the 155 BEAM-certified projects are small-scale developments. Five projects are classified as developments with an exceptional or mega-large scale.

1 The information of assessed GFAs of the certified projects in the database of the BEAM Society Ltd. was last updated in early 2020. By then, the information of assessed GFAs was available for 155 out of 193 certified projects only. 2 The categorization of the project scale follows the scale adopted in the determination of the BEAM Plus application fee. Due to limited available clarification on the BEAM's assessed GFA, we assume that the construction floor area (CFA) equals the assessed GFA. 3 Projects achieving "energy use" performance refer to those projects certified under the comprehensive scheme or selective scheme ("energy use" aspect).

## *4.4. By Regions and Districts*

Table 8 presents the breakdowns of certified projects by building uses and regions. Figure 2 shows the geographical distribution of BEAM-certified projects by building uses (in dots of different colours) and the proportions of projects certified via individual certification pathways in each district. Figure 2 indicates a high concentration of the certified projects in and around Hong Kong's central business district (CBD). This can be explained by the findings in Section 4.2, which is that most of the certified projects are commercial projects. BEAM-certified single-owned Grade A office buildings are clustered in the CBD areas where Central, Admiralty, Wan Chai, and Tsim Sha Tsui districts are located. On the other

hand, from Figure 2, we can also find certified projects in different districts. This can be explained by the practices of volume certification adopted by the large developers or landlords whose green properties in their portfolios scatter citywide.

**Table 8.** Breakdowns of BEAM-certified projects by building uses and regions.

Notes: C = commercial;R=residential; I = industrial; GIC = government, institution, or community; O = other uses.

**Figure 2.** Map showing the project distribution in Hong Kong. Notes: (1) the blue circle indicates the proximate location of Hong Kong's CBD [53]. (2) Boundaries of districts in the figure follow the delineation adopted in 2016 Population By-census [54]. (3) The map was produced using QGIS 3.

## *4.5. By Completion Years and Building Ages upon Certification*

Figure 3 presents two charts, illustrating the completion years of BEAM-certified existing buildings and the information of building ages when the current certifications were obtained. Overall, the building ages of existing buildings when eco-certified are rather diverse though the certification pathway, exerting no significant impact on the building age group distribution (*t*-statistic = 0.62; *p* > 0.1). Moreover, 75% of the certifications were granted to the buildings between 11 and 40 years of age. A significant difference between commercial and residential projects can be identified by comparing the building age groups of the two sets of projects (*t*-statistic = 3.88; *p* < 0.01). That is, eco-certified residential buildings tend to be "younger" than their commercial counterparts. Moreover, 73% of BEAM EB certifications were issued to residential projects under 20 years old. Particularly, 44% of certified residential buildings were completed less than 10 years before

certification. On the contrary, only 11% of commercial buildings were less than 10 years when awarded the BEAM certification. Over half (53.2%) of the eco-certified existing buildings were commercial buildings between 21 and 40 years of age. Existing buildings built before 1970 were the HK-BEAM-certified existing Grade-A office with longstanding sustainable building maintenance (as discussed in Section 4.2) or renovated in the 2010s. To conclude, comparing the BEAM-certified residential buildings, more "old" commercial buildings are certified under BEAM Plus existing buildings. Thus, building age may be a potential barrier to sustainable building retrofits of residential buildings in Hong Kong.

## Number of Projects by Completion Year

**Figure 3.** (**a**) Project by completion year; (**b**) building age when certified by project types.

Generally speaking, more architectural and structural constraints may be encountered in the retrofitting projects of the "older" buildings. Higher costs are usually incurred in the building energy retrofits of the old buildings. Furthermore, sufficient evidence shows that the governance of old buildings (such as the old Chinese tenements) is notoriously complicated, particularly when these buildings are so-called the "three-nil buildings" (i.e., buildings not managed by any owners' corporation, residential organization, or external property management agent) [55]. This phenomenon well echoes the findings from the data analysis by completion years and building ages upon certification.

## **5. Discussion**

Drawing on the findings above, disparities have been found in the eco-certification of the existing buildings in Hong Kong. In this section, these disparities will be discussed from various perspectives, particularly the transaction cost theory and agency theory.

## *5.1. Certification of Multi-Owned Properties Impeded by Institutional Settings*

For buildings in multiple ownership, the decision to apply for eco-certification or to undergo building energy retrofits necessitates collective actions among the co-owners. Co-ownership is often regarded as a barrier to the implementation of building energy retrofits [56,57]. The difficulty to initiate eco-certification and retrofits increases with the number of co-owners involved. This echoes the classic Olsonian view that collective actions are less likely to succeed when the group size increases [36]. From a neo-institutional economics perspective, the transaction costs incurred during the negotiation and coordination could be prohibitively high when a large group of co-owners is involved. The transaction costs for initiating eco-certification applications and/or retrofit projects are lower for single-owned projects, which explains why over 70% of the certified EB projects are held in single ownership.

Besides, most BEAM-certified residential projects are of smaller scales. This also goes along with the transaction cost perspective above. The larger is the project scale, the more will be the interested parties (i.e., co-owners) involved. Higher transaction costs will then impede collective actions to partake in eco-certification. Although there are a few largescale projects in multiple ownership certified under BEAM Plus, these certifications were initiated by the property management companies rather than the co-owners themselves. The agent-led applications for eco-certification can be explained by lower transaction costs incurred in the coordination and lobbying processes. Most other cases of multiowned residential properties of smaller scales were certified under the selective scheme ("management" aspect) only. The applications were made through volume certifications by the property management companies who managed large portfolios of residential properties throughout the territory. This understanding that greening existing multiowned properties is more challenging than single-owned properties echoes many previous west studies [58].

The government intends to regard larger housing developments as more resourceful so less subsidization is provided to the large-scale housing developments to initiate building improvement projects. However, the findings of the current research may suggest that in the light of high transaction costs, which impede collective actions, more subsidization should be institutionalised to incentivise co-owners of large-scale housing developments to participate in building eco-certification and retrofit projects.

## *5.2. Agency Problems of Eco-Certification*

As discussed above, property management companies may initiate the eco-certification exercises themselves. They can obtain different "selective benefits" by choosing to participate in the eco-certification. First, the BEAM certificates can showcase their CSR initiatives for fulfilling the ESG requirements. Second, the BEAM certifications obtained by the property management companies in selected projects can serve as marketing tools for promoting the companies. Third, large property management companies have many projects in their management portfolios so they can enjoy discounts in application fees through volume certification. Fourth, as shown in Table 4, some property management companies and/or their parent groups are patron members of the HKGBC who administrates the BEAM Plus scheme. Their participation in the eco-certification exercise can demonstrate their genuine supports to the council and the scheme.

On the other hand, private sector projects tend to get less superior BEAM certifications than the projects owned by the public sector or NGOs. The private sector projects have an average score significantly higher than their non-private sector counterparts (*t*-statistics = 5.36; *p* < 0.01). Besides, most of the private sector projects are eco-certified

under the selective scheme ("management" aspect) only. These findings may indicate that property management agents are not so willing to pay efforts in achieving real energy savings. It is because their managers' remunerations (or profit margins) are set as a certain percentage (usually 10–15%) of the total operating expenses of the building (including utility charges for the common areas and facilities) [59]. In Hong Kong, electricity charge comprises a very large proportion of the expenditure in daily building management [60]. There is a strong incentive for the property management agents to keep the electricity charges high in order to maximise their managers' remunerations. Agency problems exist between the property management agents and their clients (i.e., building owners). In the lack of check and control mechanisms, the agents strive to maximise their own profits at their clients' expenses [61,62]. The agency cost of multi-owned property management is higher when more 'decision power' is dedicated to a property management agent [63]. The high agency cost, intertwined with the high transaction costs of co-owner-led certification, impedes the eco-certification of existing multi-owned buildings or developments.

Moreover, the findings of the current study may suggest that the major developers or sizeable landlords in Hong Kong are more active in eco-certification of their existing properties because of the lower agency costs incurred in the decision-making and execution of eco-certification. In many BEAM-certified EB projects, the property management agents and the developers, landlords, or building owners have close relationships (or they are in the same groups). The parties share compatible or aligned goals, so the agency costs of eco-certification or energy retrofit are comparatively lower.

One of the means to solve the agency problems is to alter the remuneration mechanism for property management agents. Instead of using a cost-plus-margin approach, a fixed amount of service fee for remunerating a property management agent can reduce its disincentive to initiate building energy retrofits. The agent can be further incentivised to retrofit with a bonus contingent on savings in energy consumption or other aspects of environmental performance improvement.

## *5.3. Greater Drives for Certification of Privately Owned Commercial Properties*

Apart from the transaction cost theory and agency theory, we attempt to draw insights in the decision making for eco-certification of existing buildings from other perspectives. In Table 3, one can see that approximately 70% of the certified existing building projects are commercial properties. Residential properties account for only 15% of the certified projects. Apparently, incentives for going green are more significant for commercial properties compared with other building uses. There are several reasons behind the unevenness across building uses. First, many commercial properties, such as offices and retail properties in Hong Kong are for leasing. It has been widely documented that green credentials help landlords attract tenants, particularly institutional tenants, to rent their properties [64,65]. Large companies, especially U.S.-based ones, are very committed to lease eco-certified properties [66,67]. Moreover, rental premiums brought about by the building eco-certifications are quite evident in the commercial property sector [68–70]. Moreover, landlords of single-owned commercial properties may be rewarded as they take the certification as CSR evidence. These drives seem to outweigh the disincentives created by the oft-mentioned dilemma of split incentives for landlords to have their properties go green [71–73].

## *5.4. More Eco-Certifications with Building Energy Retrofits in the Non-Private Sector*

The project type (or building use) is highly related to the sector and ownership type. For instance, GIC buildings, in most circumstances, are single-owned by the government departments (public sector) or NGOs. The non-private organizations tend to have their existing building projects accredited under the comprehensive scheme or selective schemes for various aspects rather than merely "Management" aspects. The non-private sector is more willing to undertake energy retrofits to their buildings, going beyond simply taking sustainable building management practices. Apart from the agency perspective discussed

above, the pattern can be explained by the motive of the public sector and NGOs. The non-private organizations would like their BEAM-certified projects to be "demonstration projects" to showcase the applications of new technologies and construction practices, to achieve sustainable building. It is important for the diffusion of such technologies and practices to the whole building sector in Hong Kong.

For the public-owned BEAM-certified buildings, they share at least one of the following features: (i) the building serves as a departmental headquarters; (ii) the building is geographically located in Kowloon East under the environmentally sustainable second CBD agenda (Energizing Kowloon East), and (iii) the building has been recently allocated public funding for refurbishment or infrastructure upgrading. The public sector projects can undergo building energy retrofits with sufficient financial resources being granted. Payback is not a necessary consideration for these public sector projects. Therefore, fewer barriers are expected in public sector projects than in private sector buildings.

Of three EB projects owned and managed by NGOs, two are headquarters buildings, one is for a business sustainability organization, and one is for a charity group. Specifically, the two buildings are single-owned and perform as role models for other organizations to follow in retrofitting their existing premises. The remaining one belongs to the Hong Kong Housing Society, a local public housing agency established in a non-governmental institutional setting. It is an old public rental housing estate renovated with the incorporation of green roofs, the use of environmentally friendly and energy-saving materials, and the introduction of an environmental and energy management system.

On the other hand, private enterprises may just target the signalling effects of the BEAM certification or take the certifications as evidence for ESG reporting. They are inclined to pick the most cost-effective options to fulfil the ESG requirements. Therefore, we can see many private enterprises applied for BEAM certification under the selective scheme for the "management" aspect only because this route necessitates minimal financial inputs. Besides, a high proportion of private enterprises opt for certification through the volume certification pathway, which offers discounts in application fees. To further promote energy retrofits among existing buildings in the city, the Hong Kong government should offer more incentives to the building owners. Apart from the current policy that capital expenditure on the installation of environmentally friendly machinery and equipment can be tax-deductible—the government may consider subsidizing building owners to apply for eco-certification under the comprehensive scheme. The rationale is that existing buildings usually need to be retrofitted first to get eco-certified under the comprehensive scheme.

## **6. Conclusions**

The research findings demonstrate the imbalance in the popularity of eco-certification among different types of existing buildings in Hong Kong. They offer insights into the areas in which the promotion of retrofits is needed. Furthermore, this study opens up a new avenue for broadening the research area of eco-certification of existing buildings. The current research unveils that there are fewer eco-certified existing buildings in multiple ownership and with larger scales. Such finding echoes the transaction cost economics and the classic Olsonian view of collective actions. Besides, agency problems are found to occur in the eco-certification of existing buildings. Property management agents tend to obtain less superior classes of eco-label or certification. This phenomenon reflects that the property management agents are reluctant to initiate energy retrofit projects with the existing buildings because real energy savings are in contradiction with their profit maximization initiatives. Based on the empirical findings, there is a need to rethink the subsidization strategy and redesign the incentive structures for third-party property management services in order to stimulate more existing buildings to be eco-certified.

While we discussed the principal–agent relationship between building owners and property management agents in the current article, agency problems in building energy retrofits also exist between the government and building owners [6]. How these agency problems shape the landscape of eco-certification remains unanswered. Furthermore, there could be some cases where the building owners may want to retrofit their properties but not pursue third-party certification of the projects [74]. There is a possible gap between energy retrofits and eco-certification, particularly for non-investor or non-corporate building owners. Thus, further investigations targeting these issues are warranted.

**Author Contributions:** Conceptualization, Y.Y. and H.H.; methodology, Y.Y.; formal analysis, Y.Y. and K.C.Y.; data curation, K.C.Y.; writing—original draft preparation, Y.Y., H.H., and K.C.Y.; writing review and editing, Y.Y., H.H., and Q.K.Q.; visualization, K.C.Y.; supervision, Y.Y. and H.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study come from the BEAM Plus Project Directory & Statistics, which are available on the Hong Kong Green Building Council's website (https://www.hkgbc.org.hk/eng/beam-plus/beam-plus-dir-stat/BEAMPlusDirectory.jsp, accessed on 21 May 2021).

**Conflicts of Interest:** The authors declare no conflict of interest.

## **References**

## *Article* **Key Performance Indicators for Evaluation of Commercial Building Retrofits: Shortlisting via an Industry Survey**

**Man Ying (Annie) Ho 1, Joseph H. K. Lai 1, Huiying (Cynthia) Hou 1 and Dadi Zhang 2,\***

**Abstract:** Key performance indicators (KPIs) are quintessentially useful for performance evaluation, but a set of pragmatic KPIs for holistic evaluation of retrofits for commercial buildings is hitherto unavailable. This study was conducted to address this issue. Built upon the findings of a systematic literature review and a focus group meeting in the earlier stages of the study, a questionnaire survey covering 19 KPIs for environmental (embracing energy), economic, health and safety, and users' perspective evaluations of building retrofits was developed. Data of the survey, collected from facility management (FM) practitioners in Hong Kong, underwent a series of statistical analyses, including Kruskal–Wallis H test, Mann–Whitney U test, and Spearman Rank Correlation. The analysis results revealed the levels of importance of KPIs perceived by different groups of FM practitioners and the rankings of KPIs. Based upon these results, eight KPIs were shortlisted, which are energy savings, payback period, investment cost, actual-to-target ratio of the number of statutory orders removed, actual-to-target ratio of the number of accidents reduced, target indoor air temperature, target indoor air quality (IAQ) class, and target workplane illuminance. These KPIs serve as keystones for further development of an analytic evaluation scheme for commercial building retrofit performance assessment. The methodology of this study can also serve as a reference for similar KPI studies in other research domains.

**Keywords:** facility management; KPI; refurbishment; renovation; retrofit; survey

## **1. Introduction**

Buildings account for 39% of all carbon emissions in the world [1]. In Hong Kong a city famous for its dense population and buildings—the volume of aged buildings is large and keeps increasing. As retrofitting those existing buildings is a sustainability goal that the international society endeavours to meet, the building industry and the government of Hong Kong have introduced various incentives that motivate building owners or operators to implement retrofits for the premises they own or manage. However, the retrofit rate of existing buildings remains low [2]. The building sector in the city is facing challenges to retrofitting the existing buildings, especially the aged buildings. One of the key challenges is the estimation of the benefits brought by the building retrofits, which relies on scientific evaluation mechanisms to evaluate the building retrofit performance against the economic input [3,4]. In the evaluation process, human decisions or judgements from owners, operators, occupants, etc. are critical elements.

Facility management (FM) practitioners are building professionals who are involved in multiple disciplines of practice to ensure the functionality, comfort, safety, and efficiency of facilities in the built environment. Their knowledge and work experience are gained through intensive interactions with the operations of the existing buildings they manage. Thus, their opinions on the change in the buildings' conditions and the best option for

**Citation:** Ho, M.Y.; Lai, J.H.K.; Hou, H.; Zhang, D. Key Performance Indicators for Evaluation of Commercial Building Retrofits: Shortlisting via an Industry Survey. *Energies* **2021**, *14*, 7327. https:// doi.org/10.3390/en14217327

Academic Editor: F. Pacheco Torgal

Received: 15 September 2021 Accepted: 29 October 2021 Published: 4 November 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

building upgrades should be valid and reliable. Although a profusion of studies has been undertaken to help building owners and operators make decisions on building retrofits, there is still limited research on establishing a set of key performance indicators (KPIs) for the development of an analytic method to evaluate the holistic performance of commercial building retrofits [5]. To address this research gap, this multi-stage study was initiated. In the initial stages, a systematic literature review was conducted to identify indicators that are applicable to performance evaluation for building retrofits, followed by a focus group meeting with FM experts, from which the experts' perceived importance of the indicators were solicited, and the practicability of the indicators in real-world commercial buildings was discussed [6]. The outcome of those preceding stages, i.e., selection of 19 essential KPIs among the 52 applicable performance indicators identified from the literature, was adopted to design a questionnaire survey. Distributed to FM practitioners in Hong Kong, the survey intended to solicit opinions from a large sample of industry professionals on their perceived importance of the 19 KPIs. This paper, reporting on the works undertaken for this survey, is structured as follows: Sections 2 and 3 show the literature review and the data collection method; Sections 4 and 5 present the statistical analyses and results of the analysed findings; Section 6 discusses the implications of this study; Section 7 concludes the paper.

## **2. Building Retrofit**

## *2.1. Definitions, Scope, and Challenges*

Building retrofit, a form of technical intrusion in the systems or structure of a building after its initial construction and occupation [7], can improve the building's performance to optimise energy utilisation and enhance users' occupation experience. In recent years, building retrofit is increasingly referred to as green retrofitting or sustainable retrofitting, which emphasises the environmental benefits of retrofitting work in the built environment. The US Green Building Council (USGBC) defines green retrofitting as ' ... any upgrade of an existing facility to improve energy and environmental performance, decrease usage of water and enhance existing comfort and quality of interior spaces—All achieved in a manner that provides financial incentives to the investor' [8]. Common building retrofit projects include energy efficiency retrofits (e.g., upgrading of the building envelope) [9], lighting retrofits [9,10], ventilation system retrofits [10], water efficiency retrofits [11], renewable energy installations [9,10], green roof establishment [12], building automation and control system [13], and space utilisation and reconfiguration.

In general, building retrofit has been facing numerous challenges, including but not limited to, optimisation or fulfilment of priorities of stakeholders, time period, capital investment, cost effectiveness, risk analysis, technology availability, government policies, and building energy performance prediction [7]. From a process management perspective, these challenges are influenced by each other in an interactive manner and some of them incur extra costs, which can become liabilities to the building owners or its occupants. In order to support building retrofit decisions, various evaluation methodologies have been developed to provide an estimation of the associated energy consumption and cost, thereby facilitating the building retrofit process design [14]. The manifestation of such challenges varies with the project or building specificities, such as building types (e.g., public building, residential building, and office building), building design (including both system and structural design), building material, and building technologies [15,16].

## *2.2. Performance Measurement*

In the past decade, a considerable number of studies have been conducted in evaluating the performance of building retrofits. Decision making is a prevailing stream of studies in the field of building retrofits. Economic viability is an indicator frequently used for building retrofit performance measurement. Net present value (NPV), internal rate of return (IRR), overall rate of return (ORR), benefit–cost ratio (BCR), discounted payback period (DPP), and simple payback period (SPP) are often used to assess the economic

feasibility of a single retrofit measure [15,17–20]. Surveys on users' satisfaction or feedback from stakeholders in the post-occupancy phase were also used to measure building retrofit performances [21,22].

On top of that, the KPI approach is regarded to be one of the most popular and valuable tools for measuring the process or outcome of construction projects. KPIs are a collection of indicators that can comprehensively reflect a project's goals. They help to define the nature, scope, expected quality, and unique characteristics of the projects and can also provide means for measuring the 'progress towards those goals for further learning and improvement' [23]. Energy performance and energy saving are two common KPIs for measuring the financial and environmental benefits of building retrofits [24–28]. As sustainability is one of the key project goals, an increasing volume of studies in the literature have examined KPIs for measuring the level of sustainability in construction and building renovation/retrofit projects. Kylili et al. [23] provided a state-of-the-art review on the KPIs identified for measuring the sustainability of the projects in the built environment, in which they categorised the building performance KPIs into eight groups namely, economic, environmental, social, technical, time, quality, disputes, and project administration. Al Dakheel et al. [29] conducted a review on features of smart buildings (SBs) and identified 10 KPIs for SBs. The KPIs they identified help to quantify the 'smart features' of SBs and reflect the 'smart capability' of the building. The validity of KPIs affects the overall measurement results; thus, the selection process of KPIs should engage scientific methodologies to ensure their representativeness of the measurement goals [30]. Industry experts' involvement is regarded as one of the reliable approaches for identifying representative and valid KPIs. This approach usually entails three steps: (1) interviews with experts to define the measurement goals and identify KPIs that fit those goals; (2) a survey to collect a wider scale of data from various groups of experts; (3) statistical analyses to confirm and verify the identified KPIs from the previous steps. Xu et al. [31] followed these three steps to identify KPIs for the sustainability of building energy efficiency retrofit (BEER) in hotel buildings in China. Lai et al. [32] used the same approach to investigate KPIs for measuring the performance of hospital facilities management. This study, likewise, adopted this approach to identify and verify KPIs for the evaluation of commercial building retrofits.

## *2.3. The Role of FM Practitioners*

A building retrofit project usually comprises five major phases: project set up and pre-retrofit survey, performance assessment, identification of retrofit options, site implementation and commission, and validation and verification [15]. Completion of a retrofitting project requires a team of building experts to assess the existing building conditions, design the retrofitting strategies, monitor the retrofitting process, and review the project outcome [33]. In this process, FM practitioners deal with daily building management activities at the operational level and are involved in developing cost-effective plans to support built asset management at the strategic level. For example, FM managers are engaged in a company's corporate social responsibility strategy development through evaluating the facility performance of the property portfolio. They are responsible for providing advice to top management on green certification decisions and participate in obtaining green certification. Building retrofit is an inevitable activity that leads to green certification for existing buildings.

Responsible for managing both buildings and the relevant stakeholders (e.g., owners, occupants, tenants), FM practitioners have to communicate with retrofitting decision makers and facility users, modify facilities, upgrade systems for energy use, and develop mechanisms for measuring energy consumption, monitoring energy use process, and assessing energy performance [34,35]. Thus, FM practitioners play critical roles in supporting decision making on building retrofits, and their opinions on KPIs for building retrofit performance are useful.

## **3. Data Collection**

In the preliminary stages of this study, 52 performance indicators for building retrofit performance assessment were identified through the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) process and these indicators were grouped into four categories ('economic', 'environmental', 'health and safety', and 'users' perspective') with their detailed meanings provided [6]. With these indicators identified and in order to establish a method that can evaluate the holistic performance of commercial building retrofits, a focus group study was then conducted. Grounded upon the deliberations and opinions of the focus group study, 19 KPIs were selected as useful for building retrofit performance assessment (Table 1). Since using 19 KPIs entails a considerable effort to collect the associated empirical FM data, and the process for calculating this large number of KPIs is time consuming [36–38], a questionnaire survey was designed to shortlist indicators that are useful for reflecting the performance of commercial building retrofits.

**Table 1.** Performance indicators selected from the preliminary stages of the study.

The questionnaire consists of three parts. Part 1 collects respondents' personal information, including gender, years of work experience, job level, nature of their organisation, type of employer, and their academic qualification. These pieces of information served to reflect the backgrounds of the respondents, allowing inter-group comparisons to be made when analysing the survey findings. Part 2 solicits the importance ratings of the 19 KPIs on a five-point scale (1: very low; 2: low; 3: moderate; 4: high; and 5: very high). Part 3 asks the participants to suggest any other KPIs they consider important and any other comments they have based on their experience.

Pilot tests, with the participation of five FM experts, were conducted on the questionnaire. These tests helped to detect and eliminate any potential error or misunderstanding of the questions in the survey. Feedbacks from the tests were taken to finalise the questionnaire before its official distribution. The industry-wide online survey was officially launched in two ways: snowballing and mass email. Using a snowballing approach, FM professionals who participated in the preceding focus group study [6] and pilot tests were invited to complete the survey and also distribute it to their colleagues. As regards the second approach, mass email, a hyperlink to the survey was emailed to the members of the Building Services Operation and Maintenance Executives Society (BSOMES)—the leading professional body in Hong Kong specialised in technical FM works embracing building retrofits. In order to increase the level of representativeness of the samples, FM practitioners with different organisational natures (government, non-governmental organisation (NGO), and private company) and types (e.g., owner/developer, management company, and contractor) working at different levels (strategic (e.g., director, chief engineer), tactical (e.g., manager, engineer)) were invited to participate in the survey. The demographic details of the survey respondents are shown in Table 2.

**Table 2.** Demographic details of respondents.

A total of 164 responses to the survey were received. To ensure data quality, the responses were screened manually, and those with incomplete information provided were discarded. This resulted in having 124 responses qualified for the subsequent data analysis. Among these responses, 83.9% were from males. The majority of the respondents were highly experienced; most were employed by private companies. The proportions of those working for owners/developers and management companies were comparable, while those working for contractors amounted to 14.5%. When comparing the strategic and tactical groups, the latter prevails. More than three-quarters of the participants have

worked on office buildings; nearly half have worked on retail premises. The respondents were well educated, with most of them possessing a degree at the bachelor level or above.

## **4. Statistical Analysis**

The data collected were analysed using the SPSS version 26.0 software. To investigate any differences between different groups of the responses, the respondents were stratified into six groups. Each of these groups was further categorised into subgroups ('*n*' denotes the number of samples), as shown below:

First, group analyses were conducted using Kruskal–Wallis H test (H) and Mann– Whitney U test (U) to analyse whether the respondents perceived the importance levels of the KPIs differently. H test, a non-parametric test that compares more than two independent or unrelated samples [39], was applied to make comparisons between groups G2, G3, and G4. For each of the comparisons, a null hypothesis (Ho) and an alternative hypothesis (Ha) were set (Ho: there is no tendency for ranks of groups of response to rank systematically higher or lower than those of the others; Ha: there is a tendency for ranks of groups of response to rank systematically higher or lower for at least one of the groups). For testing these hypotheses, the Kruskal–Wallis H-test statistic was determined by Equation (1).

$$\mathbf{H} = \frac{12}{\mathbf{N}(\mathbf{N}+1)} \sum\_{\mathbf{i}=1}^{\mathbf{k}} \frac{\mathbf{R}\_{\mathbf{i}}^{2}}{\mathbf{n}\_{\mathbf{i}}} - 3(\mathbf{N}+1) \tag{1}$$

where N is the number of values from all combined samples, Ri is the sum of the ranks from a particular sample, and ni is the number of values from the corresponding rank sum. The α value for the H test was set as 0.05. As H test does not identify where and the degree of the differences occurred, the U test, as a type of post hoc test, was used to analyse any significant differences between the sample pairs. Following the Bonferroni procedure which helps to compensate type I error, the adjusted α, αB, was used in the U test to determine any significant difference between samples if H test was found to be significant (e.g., for G2, k = 4) [39]. The adjusted α from the Bonferroni procedure was shown in Equation (2).

$$
\alpha\_{\rm B} = \frac{\alpha}{\rm k} \tag{2}
$$

where αB is the adjusted level of risk, α is the original level of risk, and k is the number of comparisons.

For the U test, it is a non-parametric test that can be used to compare two unrelated or independent samples [39]. Therefore, it is suitable for use in inter-group comparisons between G1a and G1b, G5a and G5b, and G6a and G6b. The α value for the U test was set as 0.05. For each of the comparisons, a null hypothesis (Ho) and an alternative hypothesis (Ha) were set (Ho: the mean ranks of the groups are the same; Ha: the rank of one group of responses is systematically higher (or lower) than the other). Accordingly, the Mann– Whitney U-test statistic for each of the two samples was determined by Equation (3), and the smaller of the two U statistics was obtained.

$$\mathbf{U}\_{\mathbf{i}} = \mathbf{n}\_1 \mathbf{n}\_2 + \frac{\mathbf{n}\_{\mathbf{i}}(\mathbf{n}\_{\mathbf{i}} + 1)}{2} - \sum \mathbf{R}\_{\mathbf{i}} \tag{3}$$

where Ui is the test statistic for the sample of interest, ni is the number of values from the sample of interest, n1 is the number of values from the first sample, n2 is the number of values from the second sample, and ΣRi is the sum of the ranks from the sample of interest. The mean and z-score for the Mann–Whitney U test for large samples were found by Equations (4) and (5).

$$
\overline{\mathbf{x}\_{\mathsf{U}}} = \frac{\mathbf{n}\_1 \mathbf{n}\_2}{2} \tag{4}
$$

$$\mathbf{z} = \frac{\mathbf{U}\_{\mathrm{i}} - \overline{\mathbf{x}\_{\mathrm{U}}}}{\mathbf{S}\_{\mathrm{U}}} \tag{5}$$

where xU is the mean, SU is the standard deviation, and z is the z-score for a normal approximation of the data.

Second, Spearman's rank correlation was applied to examine any significant difference in the KPIs rankings between pairs of the respondent subgroups. Spearman's rank correlation coefficient (rs) was calculated by Equation (6) [34]:

$$\mathbf{r}\_{\mathsf{s}} = 1 - \frac{6\sum \mathbf{D}\_{\mathsf{i}}^{2}}{\mathbf{n}(\mathbf{n}^{2} - 1)}\tag{6}$$

where n is the number of observations, and Di is the difference between ranks obtained from each pair of responses. For the value of rs, '+1' represents perfect agreement between the rankings; '0' represents no association between the rankings; '−1' represents perfect disagreement between the rankings.

Finally, a mean score was calculated for each of the rated KPIs, based on which the overall ranking of the KPIs was determined to facilitate shortlisting the most essential KPIs.

## **5. Results**

## *5.1. Perceived Importance Levels of KPIs*

Referring to the results of the H and U tests shown in Figures 1–12 (see Appendix A), six significant observations are worth noting. First, significant difference (U = 701, *p* < 0.05) was found between male (mean rank = 59.24) and female (mean rank = 79.45) for KPI-11 (life cycle cost (USD)). It means that the female and male FM practitioners had different perceptions of the importance of life cycle cost for evaluating building retrofit performance. This finding echoes the argument of Rodríguez et al. [40] regarding managerial style: men and women have different managerial styles.

Second, significant difference (H = 10.538, *p* < 0.05) was found between respondents with different FM/O&M work experience for KPI-2 (normalised energy savings (kWh/m2 year)). The results show that (U = 249, *p* < 0.0125) respondents with less work experience (≤5 years; mean rank = 39.18) considered KPI-2 as more important than the experienced practitioners (experience between 20 to 30 years, mean rank = 26.38) did, and similar findings were found between freshmen (≤5 years; mean rank = 41.59) and veterans (≥30 years; mean rank = 27.74) in ranking KPI-2 (U = 262, *p* < 0.0125). This may be because the experienced practitioners were aware that after years of building occupation with energy retrofits already undertaken, the room for further energy saving is limited. Yet, no major disagreement was found between the various respondent groups (with different work experiences) on KPI-1 'energy savings (%)'. According to Miller and Higgins [41], 'percentage better and percentage saved' was mostly referenced in environmental performance evaluation studies [41].

Third, a significant difference (H = 8.726, *p* < 0.05) was found between freshmen and non-freshman (>5 years) for KPI-13 (ratio of actual to target no. of statutory orders removed (%)). The results (U = 125.5, *p* < 0.0125) show that respondents with more work experience (mean rank = 27.02) considered KPI-13 as more important than the freshmen (mean rank = 17.20). The possible reason could be the freshmen may have relatively less work experience and may not have come across any retrofit projects with the requirement in statutory orders removal. Therefore, the freshmen were less concerned about this KPI.

The other three significant differences were found between respondents at the tactical level and strategic level for KPI-17 (target IAQ class; U = 1234, *p* < 0.05); KPI-18 (target workplane illuminance (lux); U = 1159, *p* < 0.05); KPI 19 (target indoor equivalent continuous weighted sound pressure level (dBA); U = 1239, *p* < 0.05). Respondents at the tactical level perceived the three KPIs (mean rank of KPI-17 = 67.15; mean rank of KPI-18 = 68.02; and mean rank of KPI-19 = 67.09) as more important than those at the strategic level did (mean rank of KPI-17 = 51.97; mean rank of KPI-18 = 50.00; mean rank of KPI-19 = 52.11). The reason could be that FM practitioners at the tactical level have to handle and resolve complaints from users about IAQ, workplane illuminance, noise, etc. before such problems escalate to the strategic level. Hence, FM practitioners at the tactical level, when compared with the strategic counterpart, are more concerned about the KPIs in the users' perspective aspect.

## *5.2. Correlation between Rankings of KPIs*

Table 3 displays the values of the Spearman rank correlation coefficients between groups, while the detailed ranking results for different respondent groups are illustrated in Appendix A (Tables 1–3). In general, no significant disagreement was found between the rankings of the KPIs pertinent to the various groups. A significant positive correlation (at the 0.01 level) existed in the rankings between some of the respondent groups, including female vs. male; experience (>5 to <20 years) vs. experience (20 to 30 years); experience (>5 to <20 years) vs. experience (>30 years); owner/developer vs. management company; and sub-degree or undergraduate degree vs. postgraduate degree. Significant positive correlations (at the 0.05 level) existed in the rankings between some of the respondent groups, including experience (20 to 30 years) vs. experience (>30 years); owner/developer vs. others; management company vs. contractors; contractors vs. others; strategic level vs. tactical level.

**Table 3.** Spearman rank correlations between groups.

**Table 3.** *Cont*.

\*\* correlation is significant at the 0.01 level (two-tailed); \* correlation is significant at the 0.05 level (two-tailed).

## *5.3. Importance Levels and Ranks of KPIs*

Based on all the valid responses, a mean rating was calculated for each of the KPIs, and the calculation results in Table 4 show that the ratings ranged from 3.14 to 3.76.

**Table 4.** Mean importance ratings and ranks of KPIs.

To shortlist the most important KPIs for pragmatic use in building retrofit performance evaluation, 3.45 was taken as the cut-off mean rating. This rating, being the mean between 3.14 and 3.76, represents a moderate-to-high importance level. Thus, a total of 13 KPIs, covering all the four performance aspects, were shortlisted (Table 5).

**Table 5.** Scenarios of cutoff ratings.

## *5.4. Finalised KPIs*

The extraction of the most representative KPIs was based on two criteria: the rank of the KPI and the grouping category. Among the 13 KPIs, KPI-1, KPI-2, and KPI-3 can be used to indicate the energy-saving performance of a retrofit project. Among these three KPIs, KPI-1 was ranked the highest by the respondents, meaning that the practitioners regarded this KPI to be the most important, while KPI-2 and KPI-3 were only ranked the 12th and the 3rd, respectively. Therefore, KPI-1, which can cover the representations of KPI-2 or KPI-3, was taken for use.

KPI-9, KPI-10, and KPI-11 were related to the cost evaluation of a retrofit project. Thus, they can be grouped under one category. As KPI-9 was ranked the highest among the KPIs in this category, the other two KPIs were removed from the list. Additionally, when compared with KPI-9, KPI-11 (life cycle cost) is less feasible in practice because cost elements such as operating and maintenance costs, in the long run, could hardly be accurately determined at the time when a retrofit project is implemented [42].

KPI-16 and KPI-17 were related to IAQ. Despite their similar rankings (KPI-16: rank = 3 and KPI-17: rank = 5), KPI-17 (target IAQ class) covers 12 parameters for IAQ assessment and hence is more representative than a single parameter (Δ Indoor carbon dioxide levels or harmful substances (ppm) covered by KPI-16. The 12 parameters (with 10 chemical parameters) for IAQ assessment are carbon dioxide (CO2) and other pollutants—namely, carbon monoxide (CO), respirable suspended particulates (PM10), nitrogen dioxide (NO2), formaldehyde (HCHO), total volatile organic compounds (TVOC), mould, radon, and airborne bacteria. The certification of IAQ class (Good Class or Excellent Class), administered by the Environmental Protection Department of the Hong Kong government [43], is also authoritative.

For KPI-15, it was only covered in the assessment of IAQ class for building projects completed before 2019. Thus, it was an independent indicator. Participants ranked KPI-16 (Δ indoor carbon dioxide levels or harmful substances (ppm): rank = 3) over KPI-15 (target indoor air temperature (◦C): rank: 6). The reason for this may be that occupants can adjust themselves (e.g., putting on or off their clothes) to suit the indoor thermal comfort condition, while they can hardly notice the concentration of the carbon dioxide or harmful substances, not to mention removing such substances. Participants may, therefore, perceive KPI-16 as more important than KPI-15 for building retrofits.

The rest of the original KPIs, i.e., KPI-6, KPI-13, KPI-14, KPI-15, and KPI-18, are independent indicators without overlaps. Thus, they were retained on the KPIs list. The original 13 KPIs and the final eight KPIs are shown in Table 6.

Note: \* indicates the finalised numbering system.

After the foregoing activities (literature review, focus group, and survey), the shortlisted KPIs, belonging to four different aspects (environmental, economic, health and safety, and users' perspective), were determined. Correspondingly, a hierarchy for the evaluation of building retrofit performance is depicted in Figure 1.

**Figure 1.** Building retrofit performance hierarchy.

## **6. Discussion**

Part 3 of the survey, containing an open-ended question, asked the participants to provide any comments in relation to the survey topic. From the responses collected, two types of barriers to building retrofits were identified: (1) high evaluation cost and difficulty of obtaining precise cost estimation; (2) different natures of retrofit projects.

## *6.1. High Evaluation Cost and Difficulty of Obtaining Precise Cost Estimation*

The core information to support building retrofit performance assessment is related to safety and proper working of the built assets, health and comfort, space functionality, and energy. Such information is usually gathered by FM managers who arrange maintenance works or technical inspections, or by users who report complaints and fill in satisfaction questionnaires. The main purpose of this is to help improve performance during the operational phase of a building [44]. However, systematic collection of all necessary data to support building retrofit performance evaluation is costly, as one of the survey respondents stated:

' . . . initial cost (for evaluating the building retrofit performance) can be high . . . '

Although data acquisition can be simple using modern and powerful computerised systems [45,46], data overload can be a problem when a sophisticated data mining algorithm is needed to obtain useful information [47]. Thus, whether collecting the data for performance evaluation is worthwhile is a common decision to make for FM managers [45]. If all the applicable KPIs identified from the literature are used to evaluate the performance of building retrofits, considerable effort and resources will be needed to obtain and process the data [37]. Kumar et al. [45] also considered that having many indicators was impractical, and indicators should be simple to allow performance benchmarking [45].

Moreover, it is difficult to accurately predict the energy consumption of commercial buildings. A survey respondent who worked for a private management company stated:

'It (building retrofit) cannot give the precise percentage as the change of the weather might cause the energy consumption to increase significantly. For the energy-saving aspect, we do keep at 2% per year depending on electricity side only.'

As submeters are not commonly installed to monitor the energy consumption of different parts of building services systems [6]. The additional cost of installing submeters, for example for a centralised chiller plant being retrofitted, is usually high given the need to modify the relevant part of the existing system [48]. Therefore, measuring the actual energy saving with respect to the retrofitted portion of the centralised system could be difficult.

## *6.2. Different Natures of Retrofit Projects*

It is common to upgrade the existing equipment at the end of the equipment life or when it comes to failure. Traditional retrofits practice focuses on replacing particular equipment such as chillers and lighting, instead of maximising overall building performance [49]. Additionally, the initial physical condition of the substituting equipment is often emphasised. This is consistent with the following opinion collected from the survey:

'Many retrofit projects initiation was based on the order of equipment or system end of life, no spare part support, change of use/demand, justifiable energy saving whereas functional or environmental enhancement are usually in the lowest priority.'

The following statements from the survey respondents further indicated that the applicability of the KPIs in performance evaluation may vary with the nature and scope of the retrofit projects.

'The ... answers (for KPIs) were generic, in fact, the scores should be depending on the nature of the retrofit project.'

'Some KPIs may not be commonly used or considered by management, and some may not be applicable when designing the retrofit project, while some may be irrelevant to the reason for carrying out retrofit.'

'Special attention should be taken in case of the retrofit project carrying out phase by phase as the newly added and the existing system may be connected and worked together at the same time. Final commissioning of the whole system is necessary at the final stage of the project.'

From the managerial perspective, building performance depends on the resources (e.g., financial, technological, and labour) that are available and the quality of service that should be achieved [50]. In any case, facilities must be assessed with an organisation's goal and mission in place, and the assessment result should inform how well the facilities help the organisation meet its goal and fulfil the mission [51,52].

## **7. Conclusions**

In this study, an industry-wide online survey was conducted to solicit the opinions of FM professionals on the KPIs that are applicable to commercial building retrofit performance evaluation. The results showed that the professionals were generally positive about the importance of the 19 KPIs (with mean ratings being 3 (moderate) or above on a five-point Likert scale). The survey data were analysed through conducting U and H tests, as well as Spearman's rank correlation; the analysed results revealed the variations in the importance of the KPIs perceived by the different groups of professionals.

To enable an effective performance evaluation of building retrofits, the 19 KPIs were further shortlisted. Aside from rating the importance levels of the KPIs, the survey respondents were also invited to provide any other comments about KPIs for building retrofit performance evaluation. Built upon such comments (qualitative) and the KPIs' importance levels (quantitative), a final list of eight KPIs was determined. These KPIs are able to critically reflect the performance of building retrofits in the environmental (energy) aspect and also three other aspects (economic, health and safety, and users' perspective).

Overall, this study contributes to the identification of pragmatic KPIs for commercial building retrofit performance evaluation and serves as a keystone for further development of an analytic evaluation scheme for the assessment of building retrofit performance. Using these KPIs, case studies can be further conducted to examine their applicability in assessing retrofit performance for commercial buildings. To this end, the assessment weighting of each of the KPIs needs to be determined, for example, by an analytic hierarchy process or an analytical network process [53,54], for which the data required can be solicited through interviews with experts working on building retrofits. Furthermore, the methodology of this study can serve as a reference for similar KPI studies in other research domains.

**Author Contributions:** Conceptualisation, M.Y.H. and J.H.K.L.; methodology, M.Y.H. and J.H.K.L.; validation, M.Y.H., J.H.K.L., H.H. and D.Z.; formal analysis, M.Y.H. and J.H.K.L.; investigation, M.Y.H.; data curation, M.Y.H.; writing—original draft preparation, M.Y.H., J.H.K.L. and H.H.; writing—review and editing, M.Y.H., J.H.K.L., H.H. and D.Z.; visualisation, M.Y.H.; supervision, J.H.K.L.; project administration, M.Y.H. and J.H.K.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** The work reported above is supported by the Department of Building Environment and Energy Engineering of The Hong Kong Polytechnic University.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **Appendix A**

*Energies* **2021** , *14*, 7327

**Figure 1.**

 H-test

 G2

*Energies* **2021**

, *14*, 7327

**Figure 8.**

Mann–Whitney

 U-test results of G2 (work experience:

 G2b vs. G2c).

**Figure 9.**

Mann–Whitney

 U-test results of G2 (work experience:

 G2b vs. G2d).

*Energies* **2021** , *14*, 7327

 G2).

 G6).

*Energies* **2021** , *14*, 7327

## **References**

## *Article* **Case Study and Feasibility Analysis of Multi-Objective Life Cycle Energy System Optimization in a Nordic Campus Building**

**Vilppu Eloranta 1,\*, Aki Grönman 2 and Aleksandra Woszczek 2**

	-<EMAIL_ADDRESS>(A.G<EMAIL_ADDRESS>(A.W.)

**Abstract:** Due to the high energy consumption of buildings, there is a demand for both economically and environmentally effective designs for building energy system retrofits. While multi-objective optimization can be used to solve complicated problems, its use is not yet widespread in the industry. This study first aims to develop an efficient and applicable multi-objective building energy system optimization method, used to dimension energy production and storage retrofit components in a case campus building in Lahti, Finland. Energy consumption data of the building are obtained with a dynamic energy model. The optimization model includes economic and environmental objectives, and the approach is found to function satisfactorily. Second, this study aims to assess the feasibility and issues of multi-objective single-building energy system optimization via the analysis of the case optimization results. The results suggest that economically beneficial local energy production and storage retrofits could not always lead to life cycle CO2-eq emission reductions. The recognized causes are high life cycle emissions from the retrofit components and low Nordic grid energy emissions. The performed sensitivity and feasibility analyses show that correctness and methodological comparability of the used emission factors and future assumptions are crucial for reliable optimization results.

**Keywords:** building energy system optimization; renewable energy retrofit; life cycle emission

## **1. Introduction**

Buildings and their construction consume over one-third of the total global energy consumption and cause almost 40% of the global carbon dioxide (CO2) emissions [1], and 36% of the CO2 emissions in the European Union (EU). The EU Energy Performance of Buildings Directive (EPBD) already requires new buildings to be nearly zero-energy buildings (NZEB); however, the EU building stock is also rather old and slowly renovated. Therefore, retrofits and modernizations to existing properties offer large energy performance and sustainability potential [2]. Thus, there is a need for reliable methods to identify the best-performing retrofit and modernization targets.

In a typical Finnish design, on-site renewable energy production, especially photovoltaic (PV) generation, is dimensioned to maximize self-utilization of the produced energy in the building, and therefore, to minimize the excess production, usually exported to the grid [3]. To increase peak renewable energy production, exported energy needs to be increased, demand flexibility needs to be implemented, or storage technologies need to be utilized. If energy storage is added to the system, dimensioning becomes complicated because of component interdependency. The system state is defined not only by current energy flows but also by past energy flows that manifest in storage state-of-charge. Therefore, energy systems containing storage components cannot be easily dimensioned with traditional methods, and the design must often be assisted with simulation.

Dynamic building energy simulation software, such as IDA ICE [4], EnergyPlus [5], and TRNSYS [6], couple the indoor zone conditions and energy system state at each

**Citation:** Eloranta, V.; Grönman, A.; Woszczek, A. Case Study and Feasibility Analysis of Multi-Objective Life Cycle Energy System Optimization in a Nordic Campus Building. *Energies* **2021**, *14*, 7742. https://doi.org/10.3390/ en14227742

Academic Editors: Cynthia Hou and Gianpiero Colangelo

Received: 5 October 2021 Accepted: 15 November 2021 Published: 18 November 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

timestep. Dynamic models can include the entire building with its HVAC systems and onsite energy sources, including solar and geothermal. The software can be used for various purposes, such as dimensioning the energy system components, assessing indoor climate conditions, or verifying certifications. However, once the problem becomes truly dynamic and interdependent with storage systems, fully dynamic approaches are computationally intensive. One approach is to draft multiple scenarios with different component sizes, and run the full dynamic simulation for each case [7]. The optimal case may be chosen based on the results. However, this brute force approach requires a lot of manual work and computational resources and might, therefore, not be feasible in some applications.

This problem can be solved through separating the optimization from the dynamic model. Several multi-stage energy system optimization approaches for buildings have been recently studied, utilizing dynamic energy modeling tools such as EnergyPlus [8–11] or IDA ICE [12] to obtain the demand data. Optimization is then performed with separate tools, such as MATLAB, either with or without linking it to the dynamic simulation software. Studied and tested algorithms include linear programming (LP) [13–15], mixed-integer linear programming (MILP) [8,16,17], genetic algorithm (GA) [9,10,12,18], and particle swarm optimization (PSO) [11,19].

Energy system optimization approaches have not yet become widespread in practical system design processes. Challenges of utilizing energy optimization in these workflows have been analyzed via specialist interviews [20] and literature reviews [21]. The interview study [20] found various issues, such as variation of methods, high required expertise, validity concerns, required computation and time resources, lacking or poor-quality input data, and lack of production-ready tools. Nguyen et al. [21] state problems such as missing interfaces between simulation and optimization software, difficulty of adequate trade-offs between accuracy and simplicity, and lack of policies enforcing the use of optimization. Moreover, integration of life cycle emission considerations to building design processes is problematic due to inconsistent values and methods. Identified issues include noncomparable reporting standards, operational vs. embodied emissions, and region-specific and inadequate calculation tools [22].

Due to the old EU building stock and the need to improve its energy performance, energy retrofits and their optimization has been studied broadly. Ibañez Iralde et al. [23] reviewed the current energy retrofit measures and funding for residential buildings in Spain, and specified retrofit possibilities. Galatioto et al. [24] reviewed the feasibilities of energy retrofits in historical Italian buildings, considering insulation, energy storage, and intelligent control actions, among other possibilities. Gagliano et al. [25] compared the configuration scenarios of PV panels, battery capacities and energy consumption profiles in residential applications to minimize energy exchange with the grids. He et al. [26] presented a comprehensive retrofit analysis considering wall insulation, piping system, occupancy sensors, and other means. Nocera et al. [27] performed a retrofit analysis for a hotel in Italy, studying broad actions to achieve nearly zero-energy building (nZEB) status. Pirmohamadi et al. [28] demonstrated an optimization approach for an office solar thermal system in Iran to optimize energy consumption, and economic and environmental costs. These studies, however, take place outside the Nordic conditions characterized by cold climate and relatively low electricity and heat energy prices. Moreover, the introduced studies do not consider life cycle emissions from the retrofit equipment manufacturing.

From these premises, this study first aims to develop a computationally efficient and applicable multi-objective building energy system optimization method for energy system component dimensioning, utilizing balanced economic and environmental optimization objectives. The approach is based on linear optimization that can be applied in early design phases for either new construction or retrofit and requires either simulated or measured energy demand data of the building as an input. It extends previous research by developing a modular and user-friendly template, utilizing built-in components from the open-source energy system modeling framework Calliope [29]. The approach is used to dimension energy system component retrofits for a campus building in Lahti, Finland, and environmental life cycle optimization is enabled by obtaining component life cycle emission data from literature. Second, this study aims to assess the feasibility of economicenvironmental multi-objective single-building energy system optimization by analyzing result reliability and comparability. The process is enabled by sensitivity, feasibility, and qualitative analyses of the case optimization results, from which potential actions and best practices are derived and discussed.

The article is organized as follows: first, the applied modeling approaches are presented, including the model validation and used parameters. Second, the optimization results are presented along with sensitivity analyses and further insights. Last, the results are discussed along with future research needs and conclusions.

## **2. Methodology**

The case building in Lahti, Finland is a repurposed furniture factory, currently accommodating production, office, storage, and campus spaces. Floor plans of the building are available in Appendix A. The building was originally built in the 1950s and partly modernized in the late 2010s, when 111 kW photovoltaic (PV) solar panels and a 1 MW ground source heat pump (GSHP) system with ground boreholes were installed. The building is connected to the local district heat (DH) network.

As the building consists of several parts and is partially renovated, it contains structural parts from different eras, differing in heat conductivity. Original windows have double glazing, i.e., inner and outer frames with single panes, whereas modernized windows have triple glazing, i.e., inner frames with two pane insulating glass elements and outer frames with single panes. Large glazing areas have solar control glass.

Due to the northern location of Finland, the achievable PV production profile is distinct with high insolation in the long summer days, and low in the short winter days [30]. Hence, utilizing excess PV electricity production in the summer to drive heat pumps and storing the generated heat seasonally could be one solution to balance this characteristic. Combining PV production and heat pump with battery [31] or thermal storage [32] have been studied; however, the integration of these systems in Nordic conditions still needs further research. The feasibility of this concept is investigated in the results.

As this study focuses exclusively on the building energy system, other means of energy performance improvement, such as renovation, insulation improvement, occupant coaching, or schedule optimization, are not considered.

## *2.1. Dynamic Building Energy Model and Validation*

Energy demand data for the building were obtained with dynamic building simulation software IDA Indoor Climate and Energy (IDA ICE) [4]. The building geometry was derived from floor plans and by site inspection. Thermodynamic properties, such as thermal conductivities, were sourced from structural design documentation and by inspection, and they are presented in Table 1. The model floor area is 79,184 m2 and the volume 374,643 m3.

**Table 1.** Areas and heat conductivities of the building structures in the dynamic energy model. Heat conductivity is presented as the average value with the range of various structures in parentheses.

The default energy plant model of IDA ICE was used, containing simple heating and cooling heat exchangers with unlimited capacities. Heating distribution fluid temperature was set to adjust linearly based on the ambient temperature, from 70 ◦C at −30 ◦C ambient to 20 ◦C at +20 ◦C ambient, and the zones were heated with water radiators with 21 ◦C setpoints. Indoor cooling was performed mainly via chilled supply air with 24 ◦C zone setpoints. The standard IDA ICE air handling unit model was used, implementing a heat recovery exchanger with 50% efficiency and supply air heating and cooling. Constant supply air temperature was set at 17 ◦C and fan operation schedule to 6:00–19:00 on workdays. Office and learning spaces were configured for temperature-controlled variable air flows from 2 L/(s m2) to 4 L/(s m2), and standard spaces with constant air flows of 2 L/(s m2). Occupancy, internal load, and indoor lighting schedule was set to 7:00–19:00 on workdays. ASHRAE IWEC2 weather data [33] for Lahti, Finland, and the default IDA ICE urban wind profile were used. The model construction, visible in Figure 1, was simplified by combining zones and windows to ensure practical simulation times with satisfactory resolution. The simplified model contains 111 window and 70 opening or door elements.

**Figure 1.** Dynamic campus building energy model constructed in IDA ICE. Walls, roofs, doors, and windows are visible in the three-dimensional representation. The long facade in the forefront is facing west.

The model was validated by comparing monthly simulated space heating and cooling energy consumptions with measured data from the building, which includes GSHP heating and cooling and imported district heat energies for the year 2020. In Finland, annual variations of space heating energy consumption data are standardized according to [34]:

$$Q\_{\rm std} = \frac{S\_{\rm N}}{S\_{\rm R}} Q\_{\rm R} + Q\_{\rm DFAW} \tag{1}$$

where *Q*std is the standardized space heating energy consumption, *S*N the average reference value for the location, *S*R the realized reference number for the location, *Q*R the realized energy consumption for the current year, and *Q*DHW the domestic hot water energy consumption to be excluded from the standardization. Since the available measured data are consolidated with no possibility to separate the energy used for DHW heating, the correction was performed inversely on the simulated data to convert them to the year 2020. Therefore, *Q*R is solved by Equation (1), resulting in:

$$Q\_{\rm R} = \frac{S\_{\rm R}}{S\_{\rm N}} (Q\_{\rm std} - Q\_{\rm DHW}) \, \tag{2}$$

where *Q*std is now the energy consumption input from the simulated data.

The relative reference values for Lahti, Finland for the whole year of 2020 are *S*R = 3438 and *S*N = 4392 [35], indicating a warmer year than usual. In the model validation, the monthly heating degree days values for the year 2020 [35] were used. Monthly comparison of measured energy consumption data and corrected simulated values for model validity assessment is presented in Table 2.

**Table 2.** Corrected simulated monthly energy consumption data from IDA ICE is compared to the measured data from 2020. The simulated values are corrected to represent the year 2020 through the heating degree days correction. The data are used for the validation of the energy consumption model.

Although some monthly and seasonal variance is visible in the data in Table 2, the annual difference of −4.2% and the similar trend shapes between measured and simulated data were considered to validate the energy consumption model for use in this study.

## *2.2. Modular Energy System Model*

The building energy system model was constructed with an open-source linear programming energy system modeling framework Calliope [29]. The framework enables multi-objective optimization with a linear programming solver of choice; in this case, COIN-OR Branch-and-Cut (CBC) solver was used. The problem was defined as linear programming (LP) instead of mixed integer linear programming (MILP), since values such as storage capacity can reduce to zero to represent an absent equipment, and no binary investment costs are included.

The model is based on energy balances of each modeled component, fulfilling the energy balance at each timestep for each separate location, technology, and energy carrier. In this single-building case, there is only one location, and the balance is written as follows [36]:

$$E\_{\text{prod}}(\text{tech} \dots \text{carrier}) + E\_{\text{con}}(\text{tech} \dots \text{carrier}) + E\_{\text{expport}}(\text{tech} \dots \text{carrier}) = 0 \tag{3}$$

where for a specific technology in an energy carrier (*tech* :: *carrier*), *E*prod is the energy produced in or brought to the node, *E* con is consumed, and *E*export is exported.

In this case, three main energy carriers were included in the model: electricity, heating, and cooling. The heat carrier is divided into high- and low-temperature subcarriers: hightemperature heat is always imported from district heat, while low-temperature heat can be sourced from GSHP or storage. At least 50% of DHW heating power is always sourced from the high-temperature subcarrier. The overall structure of the model components and carriers is presented in Figure 2.

**Figure 2.** Diagram of the energy system optimization model for this specific case. The main component enabling flexibility is the ground source heat pump, which links separate energy carriers through its energy conversion ability. In this model, energy demands are pre-defined from the dynamic building model data.

The model configuration optimizes three parameters: PV panel peak power, heat storage capacities, and electricity storage capacities. The system's lifetime is 25 years and the real interest rate is 5%. Emissions are considered with no depreciation rate. The following subsubsections describe the individual components modeled in the energy system model, as well as their mathematical formulations.

## 2.2.1. GSHP Model

The GSHP is a central component in the model, as it enables power-to-heat conversion when excess PV production is available. It was simplified as an energy conversion component with constant conversion ratios, following Equation (4) [36] in each timestep. Using the constant coefficient of performance (COP) for heating and energy efficiency ratio (EER) for cooling is reasonable, as the heat pump evaporator and condenser temperatures stay quite constant in operation.

$$\sum\_{\text{tucl:carrier},\text{out}} \frac{E\_{\text{prod}}(\text{tlech} :: \text{carrier})}{\text{R}(\text{tlech} :: \text{carrier},\text{out})} = -\sum\_{\text{tucl:carrier},\text{in}} E\_{\text{Con}}(\text{tech} :: \text{carrier}) \times \text{R}(\text{tlech} :: \text{carrier},\text{in}) \times \eta(\text{tech})\tag{4}$$

where *R* is the set carrier conversion ratio. Here, the ratios correspond to COP and EER and include the conversion efficiency *η*(*tech*). Thus, this specific GSHP is modeled as:

$$\frac{E\_{\text{prod}}(\text{GSHP} \colon \text{heat})}{\text{COP}} + \frac{E\_{\text{prod}}(\text{GSHP} \colon \text{cooling})}{\text{EER}} = -E\_{\text{con}}(\text{GSHP} \colon \text{electricity}) \times 1 \tag{5}$$

The measured GSHP production data from the site data showed a combined productionweighted average COP/EER 3.8, calibrated IDA ICE GSHP model COP 3.3 and EER 4.8, and system datasheets COP 3.3 and EER 4.3 at design operating points. The datasheet values were selected and resulted in approximately 50 ◦C condensing temperature both in the IDA ICE model and datasheets.

In GSHP systems, too intense heat removal can lead to freezing of borehole water and the surrounding soil [37], and therefore, the borehole heat extraction power must be limited. Real systems are often controlled with pre-set operation modes for different ambient temperatures. In the location, geothermal potential is approximately 7 kW per one 300 m borehole [38], which scales to 368 kW for the entire borehole field. Maximum GSHP compressor power of 300 kW fulfilled the condition on annual level.

## 2.2.2. Solar Generation and Energy Storage Technologies

The modeled PV capacity was constrained to a range from the present 111 kW to the maximum that was estimated to fit the building roof, i.e., 555 kW. Widely commercially available mono- or polycrystalline silicon panels were chosen, for which price and carbon dioxide-equivalent (CO2-eq) life cycle emission data are widely available. The used parameters for the PV production component are listed in Table 3.

**Table 3.** Boundaries and specific parameter for photovoltaic systems.

Thermal storage was modeled as a water tank sensible heat storage, as the other alternative, phase change material (PCM) latent heat storage, is not yet technologically mature enough. Furthermore, the available cost and life cycle emission data of PCM storages is scarce and uncertain. Electrical storage was modeled as a lithium-ion battery due to its wide commercial availability and data availability. The simulated storage energy states are defined according to Equation (6) [36], in which the current charge state is defined via the previous timestep state:

$$E\_{\text{storage}}(\text{tech}, \text{timestep}) = E\_{\text{storage}}(\text{tech}, \text{timestep}\_{-1}) - E\_{\text{con}} \eta(\text{tech}) - \frac{E\_{\text{prod}}}{\eta(\text{tech})} \tag{6}$$

where *E*storage is the charge of the observed storage and *η*(*tech*) is the technological charge and discharge efficiency including all losses. Relevant constraints and values for the thermal and electrical storage systems are listed in Table 4.

**Table 4.** Cost, emission, and performance parameters for on-site storage components.

2.2.3. Cost and Emission Factors of Energy Grid Connections

Emission factors for imported energies can be defined by accounted, average, marginal. or other values. Although electricity and district heat can be bought as accounted carbonfree products from the utilities, the actual delivered energy can be from mixed sources. In this model, average values for the connected grids were used. The current national electricity emission factor is approximately 72 g CO2/kWh [47], and the local district heat emission factor is 57 g CO2/kWh [48]. Emission factors for electricity and district heat are assumed to decrease linearly and reach zero after 25 years. Energy import costs are the total empirical values for the case, including energy, transmission, and tax components. Exported electricity compensation includes only the energy component, and thus, it is lower than the total cost of imported energy. The values are consolidated in Table 5.

**Table 5.** Cost and emission parameters for energy grid connections.

## 2.2.4. Life Cycle Cost and Emission Calculation

Life cycle costs and emissions were calculated with Calliope, following Equation (7) [36]. Emissions from maintenance operations were not considered separately, as they are not a significant part of the total life cycle emissions in this case. However, they can be included in other models or applications, if necessary.

$$\mathcal{C}(\text{cost}, \text{tech}) = \mathcal{C}\_{\text{inv}}(\text{cost}, \text{tech}) + \sum\_{\text{timestep} \in \text{timesteps}} \mathcal{C}\_{\text{var}}(\text{cost}, \text{tech}, \text{timestep}) \tag{7}$$

where *C*(*cost*, *tech*) represents the cost for a single cost type (*cost*, such as monetary or CO2-eq) and for a single technology (*tech*). *C*inv is the present value of investment and *C*var is the present value of variable costs for each considered timestep. The total system cost or emission for the entire lifetime is calculated by summing all individual technology cost values calculated with Equation (7).

## **3. Results**

The photovoltaic panel, thermal storage, and electrical storage retrofits were optimally dimensioned for the case building for three objective scenarios, which minimize the objective functions for the system lifetime of 25 years:

The optimization results are presented in Table 6. Compared to the reference scenario, in all modified scenarios, the GSHP can be at least 5% better utilized in heating due to more available renewable electricity or the added thermal storage component. Electricity storage (lithium-ion battery) is not deployed on any objective due to high life cycle costs and emissions.

As the data in Table 6 shows, in the economic scenario, maximum possible PV generation capacity and some thermal storage is installed. This setup provides a total energy import reduction of over 5% annually, but considering life cycle emissions from the equipment, it causes 6.5 tons more annual CO2-eq emissions compared to the reference scenario. This converts to a 2.6% life cycle increase. The large increase of PV production capacity leads to more electricity exports than in the reference scenario, which is especially noticeable on the self-used PV electricity ratio of 90% (approximately nine percentage points lower than in the reference results). The rather small compensation of exported electricity 0.03 €/kWh keeps the arrangement profitable.

The balanced scenario increases annual costs slightly while reducing emissions roughly by 1%, enabled by thermal energy storage at 2.5 MWh capacity. It is notable that high CO2-eq emissions from PV panel production block additional installations even in this half-environmental objective. DH imports are replaced with electricity imports due to more extensive use of GSHP heating.

In the environmental scenario, the thermal storage capacity is increased to the cap of 10 MWh, with an annual cost increase of almost 10 k€. This option seems the least desirable for the building owner, even though the GSHP can be very well utilized.

**Table 6.** Energy system optimization results and parameters for the three optimization objectives simulated in Calliope, compared to the reference case.

## *3.1. Sensitivity from Imported Energy Prices and Emissions*

Result sensitivity was assessed by varying the uncertain inputs and assumptions of the energy system model: future imported energy price, grid emission factors and their projections. The assessment was performed on three scenarios, simulated for each optimization objective to enable evaluating effects from both monetary and emission factors:

Figure 3 shows simulation results from the listed scenarios. Overall, the optimization results are very sensitive to assumptions about future energy prices and emissions. Lithiumion battery storage capacity remains at zero even in all the above scenarios.

The most apparent remark from Figure 3 is that in scenario A with environmental optimization objective, the optimized system obviously prefers importing all energy from the networks instead of installing any local production or storage equipment. This is clear, as installing new local equipment would produce life cycle emissions. However, it well represents the discrepancy of the available carbon emission values and the possibilities of creative CO2 accounting.

In scenarios B and C, the modifications cause the largest effect in the balanced optimization objective (50% monetary, 50% emission), in which the installed PV power and heat storage capacities are greatly increased due to less desirable imports. However, even in the environmental objective in scenario B, the PV power capacity is not increased due to its high specific emissions.