Evaluation of Existing Green Office Buildings in European and Mediterranean Countries

Abstract

One of the gaps in green building research in European and Mediterranean countries is the assessment of Leadership in Energy and Environmental Design (LEED)-certified projects in the context of the LEED rating system’s ongoing transition from a prescriptive to a performance-based approach. This study evaluates LEED certification strategies by analyzing the statistical association between five independent performance indicators and the overall LEED score for LEED for Existing Office Buildings version 4.1 (LEED-EB v4.1) gold-certified office projects in Sweden, Italy, Israel, Spain, Germany, and Ireland using simple linear regression. The results showed that each of the six above-mentioned countries demonstrated a unique LEED certification strategy for LEED-EB v4.1 gold-certified office projects. Linear regression revealed an unexpected result: the statistical association between the independent indicator (energy) and the dependent indicator (overall LEED) score was statistically insignificant (R² = 0.04 and p = 0.359; R² = 0.13 and p = 0.112, respectively) in LEED-EB v4.1 gold-certified office projects in Germany and Ireland. However, in Sweden, Italy, Israel, and Spain, this association was statistically significant (R² = 0.38, 0.46, 0.53, and 0.40 at p < 0.001 in all cases, respectively).

1. Introduction

1.1. Impact of the Building Sector on Environmental Damage in European Union and Mediterranean Countries

In the European Union (EU), buildings are the largest contributor to environmental pressure, accounting for more than 30% of its total environmental footprint. They are responsible “for 42% of the EU’s annual energy consumption and 35% of its annual greenhouse gas emissions”, underlining their critical role in climate change [1]. Renovating existing office spaces in the EU rather than building new ones is critical to reducing energy consumption and ensuring a healthy, comfortable, and productive indoor climate. Approximately “85% of EU buildings were built before 2000 and 75% have poor energy performance”; upgrading this existing stock is essential to reducing the 40% of total EU energy consumption attributed to buildings [2]. As of 2021, the construction industry in Israel accounts for approximately 56% of electricity consumption and approximately 33% of total greenhouse gas emissions [3]. Recently, several publications have appeared on the environmental impact of buildings in the European Union (EU), e.g., [4,5]. In addition, the EU is considering ways to significantly reduce final energy consumption, greenhouse gas emissions, the use of all mined materials, and water consumption [5].

One way to reduce the environmental impact of this sector is to make it more environmentally friendly by retrofitting existing buildings using the LEED (Leadership in Energy and Environmental Design) green rating system [6]. Gluszak et al. recently demonstrated that the LEED system has become widespread in Europe [7].

1.2. The Evolution of LEED

The LEED system has evolved from version (v) 1.0 to v5 through “v2.0, v2.2, v3, v4, and v4.1” [8,9]. LEED 2.0 and later versions provide four certification levels: certified, silver, gold, and platinum; according to LEED v3 2009 and later, these certifications require an overall LEED score of 40–49, 50–59, 60–79, and 80 points or higher, respectively. Currently, LEED v4.1 is the latest version that contains the required sample size to obtain reliable statistical inferences when performing simple linear regression.

Table 1 shows that the LEED system is undergoing significant evolution, moving away from the “prescriptive–descriptive” approach toward a “performance-oriented” approach. The aim of this shift is to ensure that buildings actually perform as designed, particularly in terms of carbon reduction, energy efficiency, and productivity.

Table 1. The transition from a prescriptive to a performance-based approach in the various versions of LEED (v1 to v5).

Version	Year	Features
LEED Pilot/v1.0	1998	Early versions of LEED (v1.0 and to a large extent v2.0) relied heavily on a prescriptive or prescriptive–descriptive approach [10].
LEED v2.0	2000
LEED v3	2009	LEED 2009 v3 moved to a more quantitative, energy-efficiency-focused approach, but the system still relied heavily on prescribed compliance paths [11,12].
LEED v4	2013	LEED v4 moved away from purely prescriptive requirements toward a performance-based approach [13].
LEED v4.1	2019	LEED v4.1 is more performance-based than LEED v4, shifting from a heavy reliance on prescriptive, design-phase documentation to data-driven, operational, and actual performance metrics [14].
LEED v5	2025	LEED v5 represents a significant shift from a prescriptive to a performance-based approach [15].

1.3. LEED Categories and Their Characteristics

Table 2 presents the LEED categories and their characteristics. LEED 2009 v3 and LEED v4/v4.1 share a common foundational structure of credit categories, although more stringent requirements, a greater focus on integrative processes, and a new location-based category were introduced in v4/v4.1. The key differences between LEED 2009 v3 and LEED v4/4.1 in terms of categories are as follows: Location and Transportation was added in LEED v4/v4.1 as a dedicated category (previously part of Sustainable Sites) and the Integrative Process category was introduced to encourage early-stage collaboration [16].

Table 2. LEED 2009 v3 and v4/4.1 categories.

Category	Features
Integrative Process (IP)	Encourages early analysis of energy, water, and site systems.
Location and Transportation (LT)	Encourages compact development, alternative transportation, and building on infill sites.
Sustainable Sites (SS)	Focuses on site management, environmental impact, and reducing pollution during construction.
Water Efficiency (WE)	Covers indoor and outdoor water consumption reduction and efficient management.
Energy and Atmosphere (EA)	The highest-weighted category, focusing on building energy performance, renewable energy, and refrigerant management.
Materials and Resources (MR)	Addresses waste reduction, sustainable material selection, and life-cycle impacts.
Indoor Environmental Quality (IEQ)	Focuses on “improving indoor air quality, lighting, and thermal comfort for occupants”.
Innovation in Design (ID)	Rewards projects for innovative design strategies not covered by other categories.
Regional Priority (RP)	Addresses specific environmental issues relevant to the project’s location.

1.4. Characteristics of Key LEED Systems

Between 2000 and 2009, four key environmental rating systems were developed for the LEED: New Construction (LEED-NC), Core and Shell (LEED-C-and-S), Commercial Interiors (LEED-CI), and Existing Buildings (LEED-EB). LEED-NC is “designed for new buildings and major renovations”; LEED-CI is used “for tenant improvements and refurbishments that do not involve the building’s shell or structure”; and LEED-C-and-S is used “for the design and construction of the building’s envelope, structure, and major mechanical, electrical, and plumbing systems, but not the interior fit-out” [17]. Finally, LEED-EB focuses on “ongoing building operations without any major refurbishments” [18]. “Originally designed as a program for following up LEED-NC-rated buildings, LEED-EB has become a stand-alone system for owners of existing buildings who wish to obtain eco-certification” [18,19].

The author of the present study focused on LEED-EB v4.1 as it places increased emphasis on the performance of existing buildings, recognizing the urgent need to retrofit the vast number of older buildings worldwide, which consume far more energy than modern structures [14].

The theoretical basis of the LEED-EB v4.1 certification system is the concept of sustainable development, focused on performance-based indicators and life-cycle-based and holistic approaches. Performance-based indicators rely heavily on actual building data (energy, water, waste, transport, and occupant comfort) [14]. The life-cycle approach is based on the sustainable life cycle of building materials and methods, promoting the creation of long-lasting solutions with low environmental impact [20,21]. A holistic approach is based on assessing the building as a whole and creating a positive environment that harmoniously combines environmental protection, social justice, and economic viability, rather than focusing on individual “green” elements [22].

1.5. LEED v4.1 for Existing Buildings

Table 3 and Table 4 list five performance-based indicators and ten prescriptive indicators, respectively. Pushkar showed that for every office project that achieved LEED-EB v4.1 gold certification, the percentage of binary performance indicators achieved was less than 1% [23]. Therefore, these ten binary prescriptive indicators were excluded from statistical analysis. It should be noted that the maximum number of points for the LEED-EB v4.1 system is 100.

Table 3. LEED-EB v4.1: five interval performance-based indicators.

Performance Indicator	Max Points	Category
Transportation	14	Location and Transportation (LT)
Water	15	Water Efficiency (WE)
Energy	33	Energy and Atmosphere (EA)
Waste	8	Materials and Resources (MR)
IEQ	20	Indoor Environmental Quality (IEQ)
Total	90

Table 4. LEED-EB v4.1: ten binary prescriptive indicators.

Prescriptive Indicator	Max Points	Category
Rainwater management	1	Sustainable Sites (SS)
Heat island reduction	1
Light pollution reduction	1
Site management	1
Enhanced refrigerant management	1	Energy and Atmosphere (EA)
Grid harmonization	1
Purchasing	1	Materials and Resources (MR)
Green cleaning	1	Indoor Environmental Quality (IEQ)
Integrated pest management	1
Innovation	1	Innovation (IN)
Total	10

1.6. Research Gap

To examine the relationship between independent LEED categories (parts) and the dependent overall LEED score (whole), most studies have used simple linear correlation, e.g., [24,25,26,27,28], while a few have used simple linear regression, e.g., [29,30]. However, all these studies were conducted on LEED-certified buildings in the United States, and the four LEED certification levels, namely certified, silver, gold, and platinum, were combined into one group before correlation analysis or linear regression was conducted [24,25,26,27,28]. Most studies using simple correlation or linear regression were conducted on LEED-NC v3/v4-certified projects [24,25,26,27,28], and only two focused on LEED v4.1 for Healthcare (LEEDE-HC)- and on LEED-EB-certified projects [29,30].

Below are three studies that were taken as the closest analogues to the current study.

• Puskar compared LEED-EB v4.1 gold-certified office projects in six European and Mediterranean countries and showed that each has a unique LEED certification strategy [23]. Therefore, studying LEED certification strategies in different countries is a pressing issue.
• Pushkar investigated the relationship between the built environment and the LT category in a LEED-EB v4-certified office project in the United States and showed that each LEED certification level—namely certified, silver, gold, and platinum—featured a unique certification strategy [31]. This study highlighted the need to study LEED certification strategies at different levels.
• Pushkar used simple linear regression to examine the statistical association “between individual LEED performance indicators and the overall LEED score” and identified a unique certification strategy for LEED-EB v4.1 gold-certified office projects in the United States [30]. This study showed the relevance of examining LEED certification strategies in European and Mediterranean countries using simple linear regression.

1.7. Objective, Novelty, and Contribution

The objective of this paper is to examine different LEED certification strategies using simple linear regression between independent LEED performance indicators and the dependent “overall LEED” score for “LEED-EB v4.1 gold-certified office projects” in six selected European and Mediterranean countries.

The novelty of this study is the finding that each of the six countries exhibits a unique “LEED certification strategy”, as revealed by simple linear regression analysis. Meanwhile, its practical contribution is that this knowledge will help LEED professionals perform better in these countries.

2. Literature Review

In this section, a statistical analysis of the statistical association between independent individual LEED categories (i.e., part of the whole) and the dependent overall LEED score (i.e., the whole) is presented. Section 2.1 summarizes the results of coefficients of determination (R²) calculations for the above relationship. If the results of a linear relationship between two variables were presented as a Pearson correlation coefficient (r) in the literature, they were transformed into R² values. In Section 2.2, the results of the analysis of the following simple linear regression variables are summarized: the intercept (b₀), the slope (b₁), and R². In quantitative assessments of statistical associations, special attention was paid to critical analysis of the study design.

2.1. Analysis of the Relationship Between Independent LEED Category and Dependent Overall LEED Score in Terms of R²

Table 5 shows the relationship between independent LEED categories and dependent overall LEED scores in terms of R². The EA category exhibits the strongest relationship with the overall LEED score (0.27 ≤ R² ≤ 0.63), while the WE and MR categories exhibit the weakest relationship with the overall LEED score (0.04 ≤ R² ≤ 0.15) and the LT, SS, IEQ, and RP categories exhibit an intermediate relationship with the overall LEED score (0.08 ≤ R² ≤ 0.21). The analyzed studies [24,25,26,27,28] share at least three common features: (i) the relationship between individual LEED categories and the overall LEED score is statistically significant (p < 0.001), (ii) the analysis of this relationship was conducted on LEED-certified projects in the United States, and (iii) the four LEED certification levels (certified, silver, gold, and platinum), which are four independent groups each with its own unique LEED certification strategy, were combined into one group, which was analyzed using significance tests.

Table 5. A summary of R² values obtained via post-publication analysis, assessed through a simple linear correlation between independent LEED category points and dependent total LEED points.

LEED-Certified Projects	Sample Size	Independent LEED Category
		LT	SS	WE	EA	MR	IEQ	RP	Ref.
		Versus Dependent Overall LEED Score
LEED-NC v3-certified university residence halls	87	–	0.21	0.08	0.63	0.12	0.07	–	[24]
LEED-NC v3-certified multifamily residential projects	802	–	0.16	0.05	0.46	0.05	0.07	–	[25]
LEED-NC v4-certified multifamily residential projects	75	0.18	0.08	0.06	0.27	0.04	0.18	–	[26]
LEED-NC v4-certified projects cover all building types	1252	0.12	0.13	0.09	0.38	0.15	0.14	0.16	[27]
LEED-NC v3-certified multifamily residential projects	878	–	–	–	–	–	–	0.15	[28]

2.2. Analysis of the Relationship Between Independent LEED Category and Dependent Overall LEED Score in Terms of Simple Linear Regression

Before interpreting the LEED results presented in Table 6, it is necessary to clarify the relevant terminology. LEED-NC v4.1 features the EA and MR categories as in v3 and 4, while LEED-EB v4.1 features the “energy” and “waste” performance indicators. In this case, the term “energy” is analogous to the “EA category”, and the term “waste” is analogous to the “MR category”.

Table 6. A summary of the intercept (b₀), slope (b₁), and coefficient of determination (R²), assessed through a simple linear regression between dependent overall LEED scores and independent LEED performance indicators.

LEED	Dependent Variable	Independent Variable	Regression Variable			Ref.
			b0	b1	R2
LEED-HC v4.1-certified projects in the United States	Overall LEED	EA	8.89	0.16	0.36	[29]
LEED-EB v4.1 gold-certified office projects in New York City		Energy	48.13	0.78	0.47	[30]
LEED-EB v4.1 gold-certified office projects in Washington, D.C.			44.96	0.96	0.68
LEED-HC v4.1-certified projects in the United States		MR	6.78	0.04	0.11	[29]
LEED-EB v4.1 gold-certified office projects in New York City		Waste	54.94	1.72	0.19	[30]
LEED-EB v4.1 gold-certified office projects in Washington, D.C.			53.46	1.97	0.42

Table 6 shows that the group of LEED gold-certified projects significantly outperformed the combined group of LEED-certified projects, which included four certification levels (certified, silver, gold, and platinum), with regard to linear regression parameters: b₀, b₁, and R². In both cases [29,30], there was a statistically significant (p ≤ 0.025) relationship between the independent individual LEED-HC v4.1 categories or LEED-EB v4.1 performance indicators and the dependent overall LEED score.

3. Materials and Methods

3.1. Study Design and Data Collection

The following conditions were considered when developing the study: LEED-certified projects must belong to the same region (e.g., European and Mediterranean), LEED system (e.g., LEED-EB), version (e.g., v4.1), certification level (e.g., gold), and building type (e.g., office). Office building projects that received LEED-EB v4.1 gold certification differed only by country and not by region [23].

To justify the minimum sample size when using simple linear regression to analyze environmental performance indicators in LEED-EB v4.1-certified projects, the author of [32] suggested that LEED data could be considered as part of an environmental field study. Therefore, according to generally accepted recommendations for the use of simple linear regression in ecological field studies, the minimum sample size (n) is n = 15–20 [33]. An additional argument in favor of using a sample size n ≥ 20 when performing simple linear regression is the empirical rule of regression (sample size n = 20 per variable) [34]. For this study, the minimum sample size was n = 20.

Table 7 lists six European and Mediterranean countries. As of 7 January 2026, at least 20 LEED-EB v4.1 gold-certified office projects in each country were identified from two comprehensive databases [35,36]. In a previous study, six of the twenty European and Mediterranean countries, namely Sweden, Italy, Israel, Spain, Germany, and Ireland, were selected based on the number of LEED gold-certified projects required to draw reliable statistical conclusions. LEED-EB v4.1 silver- and platinum-certified office building projects were not included in the linear regression analysis due to small sample sizes. This study is a continuation of a previous study [23].

Table 7. The distribution of LEED-EB v4.1-certified office projects across the four certification levels in six countries.

Country	Certified	Silver	Gold	Platinum
Sweden	0	9	57	0
Italy	0	0	28	2
Israel	0	0	24	1
Spain	0	2	35	11
Germany	0	0	24	3
Ireland	0	0	20	0

3.2. Statistical Analysis of Linear Regression

To conduct simple linear regression between a “part” and a “whole”, the following two assumptions should be met: linearity between the residuals and fitted values and autocorrelation in the regression residuals. Linearity was determined by superimposing a locally weighted scatterplot smoothing (LOESS) curve on the residuals alongside the plotted fitted values. If a LOESS curve is approximately clustered around the zero-horizontal line, then the relationship between the two variables is linear [37]. The results showed that the assumption of linearity was met in all cases (Appendix A, Figure A1, Figure A2, Figure A3, Figure A4, Figure A5 and Figure A6). The Durbin–Watson test was used to test for autocorrelation in the regression residuals. The results showed that the assumption of absence of autocorrelation in residuals was met in all cases (Appendix A, Table A1).

The following variables were used to interpret the results of simple linear regression: p-value, b₀, b₁, and R². If the relationship between the dependent variable and one independent variable was statistically significant (p ≤ 0.05), then the values of b₀, b₁, and R² were used to interpret the linear regression. If the relationship between the dependent variable and one independent variable was statistically insignificant (p > 0.05), then the values of b₀, b₁, and R² were not used to interpret the linear regression. In all cases, a two-tailed p-value was used. MATLAB 2024a was used to process data from LEED-certified projects [38].

4. Results

Table 8 shows that in all six countries, a positive and significant statistical association was found between several “LEED performance indicators” and the “overall LEED score”. It should be noted that each country identified a unique set of indicators that had a significant statistical association with “overall LEED.” For Sweden, these indicators are “water”, “energy”, “waste”, and “IEQ”. For Italy, they are “transportation”, “energy”, and “IEQ”. In Israel, they are “water”, “energy”, and “waste”. For Spain, they are “transportation”, “water”, and “energy”. For Germany, they are “water”, “waste”, and “IEQ”. Finally, for Ireland, these indicators are “waste” and “IEQ”. Linear regression showed the b₀, b₁, and R² values for each pair of statistical relationships. However, the practical significance of these coefficients is only meaningful if there is a significant statistical association. Figure 1, Figure 2, Figure 3, Figure 4, Figure 5 and Figure 6 present the scatterplots and regression lines for each country.

Table 8. Linear regression analyses between each of the five “individual LEED” indicators and “overall LEED” for LEED-EB v4.1 gold-certified office projects in six countries.

Country	Variable		Simple Linear Regression
	Independent	Dependent	r	p-Value	b0	b1	R2-Value
Sweden	Transport	Overall LEED	0.00	0.973	66	0.01	0.00
	Water		0.54	<0.001	54	1.07	0.29
	Energy		0.62	<0.001	41	1.04	0.38
	Waste		0.36	0.006	60	0.98	0.13
	IEQ		0.51	<0.001	51	1.19	0.26
Italy	Transport	Overall LEED	0.42	0.027	53	1.40	0.17
	Water		0.20	0.313	64	0.45	0.04
	Energy		0.68	<0.001	44	0.97	0.46
	Waste		0.17	0.374	61	1.06	0.03
	IEQ		0.49	0.009	56	0.89	0.24
Israel	Transport	Overall LEED	0.02	0.927	65	0.05	0.00
	Water		0.53	0.008	55	1.06	0.28
	Energy		0.73	<0.001	46	0.90	0.53
	Waste		0.75	<0.001	51	2.66	0.56
	IEQ		0.01	0.973	65	0.03	0.00
Spain	Transport	Overall LEED	0.45	0.007	56	1.22	0.20
	Water		0.40	0.018	64	0.60	0.16
	Energy		0.63	<0.001	51	0.76	0.40
	Waste		0.33	0.055	65	0.82	0.11
	IEQ		−0.01	0.935	70	−0.02	0.00
Germany	Transport	Overall LEED	0.16	0.450	63	0.57	0.03
	Water		0.59	0.003	55	1.29	0.35
	Energy		0.20	0.359	59	0.41	0.04
	Waste		0.63	0.001	59	2.11	0.39
	IEQ		0.52	0.009	61	0.67	0.27
Ireland	Transport	Overall LEED	0.17	0.472	64	0.43	0.03
	Water		0.18	0.459	65	0.44	0.03
	Energy		0.37	0.112	58	0.50	0.13
	Waste		0.65	0.002	47	3.43	0.42
	IEQ		0.69	0.001	56	0.90	0.47

Figure 1. Simple linear regression between five LEED individual performance indicators and overall LEED points in LEED-EB v4.1 gold-certified office projects in Sweden.

Figure 2. Simple linear regression between five LEED individual performance indicators and overall LEED points in LEED-EB v4.1 gold-certified office projects in Italy.

Figure 3. Simple linear regression between five LEED individual performance indicators and overall LEED points in LEED-EB v4.1 gold-certified office projects in Israel.

Figure 4. Simple linear regression between five LEED individual performance indicators and overall LEED points in LEED-EB v4.1 gold-certified office projects in Spain.

Figure 5. Simple linear regression between five LEED individual performance indicators and overall LEED points in LEED-EB v4.1 gold-certified office projects in Germany.

Figure 6. Simple linear regression between five LEED individual performance indicators and overall LEED points in LEED-EB v4.1 gold-certified office projects in Ireland.

The key components of variation and the regression coefficients from the statistically significant linear regression results are presented in the ANOVA (Analysis of Variance) regression tables and regression coefficient tables, respectively (Appendix B, Table A2, Table A3, Table A4, Table A5, Table A6, Table A7, Table A8, Table A9, Table A10, Table A11, Table A12, Table A13, Table A14, Table A15, Table A16, Table A17, Table A18, Table A19, Table A20, Table A21, Table A22, Table A23, Table A24, Table A25, Table A26, Table A27, Table A28, Table A29, Table A30, Table A31, Table A32, Table A33, Table A34, Table A35, Table A36 and Table A37). Statistically insignificant linear regression results were not included in these tables. The multiple regression results showed a significant statistical association between the overall LEED score and five individual LEED performance indicators (namely, transportation, water, energy, waste, and indoor environmental quality) for all six countries (Appendix C, Table A38). A comparative descriptive analysis of the six countries may help to better understand the results of the regression analysis (Appendix C, Table A39).

5. Discussion

5.1. LEED Certification Strategy in European and Mediterranean Countries

Table 9 shows that each of six countries has a unique LEED certification strategy. In Sweden, for LEED-EB v4.1 gold-certified office projects, there was a significant statistical association between four of the five performance indicators and the overall LEED score. In Italy, Israel, Spain, and Germany, there was a significant statistical association between three of the five performance indicators and the overall LEED score for the same office projects. Finally, in Ireland, there was a significant statistical association between two of the five performance indicators and the overall LEED score for LEED-EB v4.1 gold-certified office projects. It can be hypothesized that the wider range of statistical associations between independent LEED performance indicators and the resulting overall LEED score is associated with the greater diversity of LEED certification strategies [30]. Although the number of statistical association pairs is the same for Italy, Israel, Spain, and Germany (three pairs in each country), they each have unique sets of pairs. Thus, this comparison showed that LEED certification strategies in European and Mediterranean countries differ significantly from those in cities in the United States [30].

Table 9. Generalized statistical association between independent LEED indicators and dependent overall LEED score in LEED-EB v4.1 gold-certified office projects in six countries. Sig value indicates a statistically significant difference (p ≤ 0.05), and NS indicates a statistically insignificant difference (p > 0.05).

	EED-EB v4.1 Performance Indicators That Have a Statistical Association with the Overall LEED Score
Country	Transportation	Water	Energy	Waste	IEQ	Number of Statistically Significant Events
Sweden	NS	Sig	Sig	Sig	Sig	4
Italy	Sig	NS	Sig	NS	Sig	3
Israel	NS	Sig	Sig	Sig	NS	3
Spain	Sig	Sig	Sig	NS	NS	3
Germany	NS	Sig	NS	Sig	Sig	3
Ireland	NS	NS	NS	Sig	Sig	2
Number of statistically significant events	2	4	4	4	4
Number of statistically insignificant events	4	2	2	2	2

Table 9 also shows that in two countries, namely Italy and Spain, there is a significant statistical association between “transportation” and “overall LEED”, while in Sweden, Israel, Germany, and Ireland this association is statistically insignificant. A significant statistical association between “water” and “overall LEED” was found in four countries: Sweden, Israel, Spain and Germany, while no significant statistical association was found in Italy and Ireland. In four countries, namely Sweden, Italy, Israel, and Spain, a significant statistical association was found between “energy” and “overall LEED”, while in Germany and Ireland this association is not statistically significant. Furthermore, the association between “waste” and “overall LEED” is statistically significant in four countries, namely Sweden, Israel, Germany, and Ireland, but not in Italy and Spain. Finally, the association between “IEQ” and “overall LEED” is statistically significant in four countries, namely Sweden, Italy, Germany, and Ireland, but not in Israel and Spain.

5.2. Comparison of the b₀, b₁, and R² Values with Literature Data

Table 10 presents simple linear regression variables from recent and current studies on LEED-EB v4.1 gold-certified office projects. A recent study analyzed LEED-EB v4.1 gold-certified office projects in two US cities: New York City and Washington, D.C. This study observed statistically significant relationships in only two out of five independent LEED performance indicators and the dependent overall LEED score, namely “energy” and “waste” [30]. In the current study, two of the six countries, namely Sweden and Israel, exhibited a similar pattern: the “energy” and “waste” indicators exhibited a statistically significant relationship with the overall LEED score. Comparative analysis of regression coefficients showed that only Washington, D.C., and Israel had similar regression coefficients. The comparative analysis between different countries will be expanded in further research.

Table 10. A summary of the intercept (b₀), slope (b₁), and coefficient of determination (R²) for simple linear regression between independent LEED performance indicators and dependent overall LEED scores.

City/Country	Variable		Regression Variable			Reference
	Dependent	Independent	b0	b1	R2
New York City	Overall LEED	Energy	48.13	0.78	0.47	[30]
		Waste	54.94	1.72	0.19
Washington, D.C.	Overall LEED	Energy	44.96	0.96	0.68
		Waste	53.46	1.96	0.42
Sweden	Overall LEED	Energy	40.56	1.04	0.38	Current study
		Waste	60.08	0.98	0.13
Israel	Overall LEED	Energy	45.67	0.90	0.53
		Waste	51.31	2.66	0.56

5.3. Contextual Factors—European and Mediterranean Countries

It should be noted that the relationship between contextual factors (e.g., regulatory frameworks, climatic conditions, or market practices) and LEED certification strategies is an important factor for the sustainable development of green building. Among the contextual factors (e.g., energy policy in building codes, climate or market factors) influencing the choice of certification strategy, the share of energy from renewable sources can be used as an integrating factor. Table 11 shows the period from 2022 to 2024, during which the vast majority of LEED-EB v4.1-certified projects achieved certification. Table 11 shows that Sweden has the highest share of renewable energy, while Ireland has the lowest share of renewable energy. Italy, Spain, Israel, and Germany showed intermediate results in terms of the share of renewable energy [39,40]. It can be hypothesized that the increase in the share of renewable energy is related to the number of pairs in which there is a significant statistical association between LEED performance and the overall LEED score in office projects that have achieved LEED-ED v4.1 gold certification. However, further research should examine the impact of regulatory, climate, or market factors on the LEED certification strategy for LEED-EB v4.1 gold-certified office projects.

Table 11. Share of energy from renewable sources across six countries.

Country	2022	2023	2024
Sweden	69.81	67.068	67.828
Italy	19.901	20.283	20.223
Israel 1	8.58	11.93	11.84
Spain	19.332	21.904	21.572
Germany	17.391	16.909	17.677
Ireland	5.334	7.778	7.913

¹ Share of renewables in electricity production [40].

5.4. Key Results Across Countries

Table 12 shows that in Germany and Ireland there is an insignificant statistical association between “energy” and “overall LEED”, while in Sweden, Italy, Israel, Spain, and Ireland this association is significant. The lack of a significant statistical association between “energy” and “overall LEED” in Germany for LEED-EB v4.1 gold-certified office projects can be explained by the need to compensate for the high baseline energy consumption of outdated HVAC systems (Heating, Ventilation, and Air Conditioning), as well as by strict German energy efficiency standards [41]. The lack of a significant statistical association between “energy” and “overall LEED” in Ireland for LEED-EB v4.1 gold-certified office projects requires further research.

Table 12. Key results across countries.

Country	Variable		Simple Linear Regression
	Independent	Dependent	r	p-Value	b0	b1	R2-Value
Germany	Energy	Overall LEED	0.20	0.359	59	0.41	0.04
Ireland			0.37	0.112	58	0.50	0.13
Sweden			0.62	<0.001	41	1.04	0.38
Italy			0.68	<0.001	44	0.97	0.46
Israel			0.73	<0.001	46	0.90	0.53
Spain			0.63	<0.001	51	0.76	0.40
Italy	Transportation	Overall LEED	0.42	0.027	53	1.40	0.17
Spain			0.45	0.007	56	1.22	0.20
Sweden			0.00	0.973	66	0.01	0.00
Israel			0.02	0.927	65	0.05	0.00
Germany			0.16	0.450	63	0.57	0.03
Ireland			0.17	0.472	64	0.43	0.03

Table 12 also shows Italy and Spain there is a significant statistical association between “transportation” and “overall LEED”, while in Sweden, Israel, Germany, and Ireland this association is insignificant. The significant statistical association between “transportation” and “overall LEED” in Italy and Spain for LEED-EB v4.1 gold-certified office buildings can be explained by the partial substitution of gasoline-powered vehicles by electric vehicles among office workers in LEED-EB v4.1 gold-certified buildings. Replacing gasoline-powered vehicles with electric vehicles can lead to significantly lower environmental impacts, which is preferable in both the long term and in the infinite time perspective [42].

It should be noted that a significant statistical relationship between individual LEED indicators and the overall LEED score indicates to professionals which indicators can be used to achieve the desired level of certification, taking into account, of course, the achievements of other indicators that did not change with the change in the overall LEED score.

6. Conclusions

The objective of this study was to evaluate LEED certification strategies by analyzing the statistical association between five independent LEED performance indicators and the dependent overall LEED score using simple linear regression. The following conclusions were drawn:

• Each of the six countries analyzed, namely Sweden, Italy, Israel, Spain, Germany, and Ireland, demonstrated a unique LEED certification strategy for LEED-EB v4.1 gold-certified office projects.
• An unexpected result was that the association between “energy” and “overall LEED” in LEED-EB v4.1 gold-certified office projects in Germany and Ireland was statistically insignificant (R² = 0.04 and p = 0.359; R² = 0.13 and p = 0.112, respectively). In contrast, the association between “energy” and “overall LEED” was statistically significant in Sweden, Italy, Israel, and Spain (R² = 0.38, 0.46, 0.53 and 0.40 at p < 0.001 in all cases, respectively), as expected. The insignificant/significant differences between Group 1 (Germany and Ireland) and Group 2 (Sweden, Italy, Israel, and Spain) revealed by the linear regression analysis, namely between “energy” and “overall LEED” for LEED-EB v4.1 gold-certified office projects, are difficult to explain by any contextual factors (e.g., regional climate, building policy, energy structure, and other factors) due to the lack of scientific research on this issue.
• Another unexpected result was that the association between “transportation” and “overall LEED” in LEED-EB v4.1 gold-certified office projects in Italy and Spain was statistically significant (R² = 0.17 and p = 0.027; R² = 0.20 and p = 0.007, respectively). In contrast, the association between “transportation” and “overall LEED” was statistically insignificant in Sweden, Israel, Germany, and Ireland, (R² ≤ 0.03 and p ≥ 0.450 in all cases, respectively), as expected. Typically, office buildings that have received LEED-EB v4.1 gold certification are located in urban areas with developed urban infrastructure. This may explain the lack of significant statistical association between “transportation” and “overall LEED”. Thus, the positive statistically significant association between “transportation” and “overall LEED” may be driven by the substitution of gasoline vehicles for electric vehicles for workers in a number of LEED gold-certified office buildings.

7. Limitations and Future Research Directions

The results of simple linear regressions between individual LEED indicators (i.e., part of the whole) and “total LEED” (i.e., the whole) should be interpreted as structural statistical associations rather than independent explanatory effects. This study describes different LEED certification strategies in European and Mediterranean countries without taking into account external factors. Future research should examine the statistical associations among green building policies [43], climate zones [44], urban planning [45], and individual LEED performance indicators in each European and Mediterranean country.

8. Practical Implications for LEED Practitioners

Various combinations of significant statistical associations between individual LEED indicators and the overall LEED score allow professionals to identify indicators that contribute to changes in the overall score, assisting with decision-making in the early stages of LEED-certified projects.