Eco-Efficiency Model for Green Building Material in a Subtropical Climate

Abstract

Taiwan has a unique subtropical island climate. In the development of a green building material (GBM) evaluation system for Taiwan, evaluation criteria should differ from those in the frigid or temperate zones of the world. We aimed to provide academia and industry with an appropriate GBM evaluation method and to enhance the effectiveness of GBM. It means GBM should be concerned with creating more value with less impact to achieve “eco-efficiency”. Eco-efficiency is the balance of competitively priced goods and services that satisfy human needs and quality of life, while progressively reducing ecological impacts and resource intensity throughout the life-cycle within the Earth's estimated carrying capacity. We applied this method (eco-efficiency=quality/load) to comprehensively consider the efficiency of GBM. The first step was to review the literature to summarize GBM evaluation factors. Second, we conducted a two-stage survey by the fuzzy Delphi method to analyze the evaluation indicators' eco-efficiency dimension (quality or load) and the importance of evaluation factors for establishing the GBM Eco-Efficiency Evaluation System. Third, we analyzed the priority vector of the evaluation indicators by an analytic hierarchy process method before finally achieving the goal of constructing the GBM Eco-Efficiency Model. The achievement was the creation of the Taiwan GBM Eco-Efficiency Evaluation System and the GBM Eco-Efficiency Model. For academia, industry, and users, it could lead to the enhancement of GBM quality as well as provide feedback to modify the process in advance, reduce adverse environmental loads, and minimize the negative impact on the global environment. This method responds positively to human health and global sustainable development.

Introduction

Because of global climate changes and the frequency of abnormal weather occurrences and disasters around the world, sustainability has become a topic of international attention (Hart and Ahuja, 1994). Discussions of globally debated environmental topics have become less passive with more proactive dialogues on sustainability (Hanssen, 1999). Taiwan has actively proposed environmental response policies, including plans to create ecological cities through green building evaluation and the use of green building material (GBM), which is a basic feature of green building projects, because these materials have the greatest range of application and can directly affect both human health and sustainability (Ho et al., 2008).

Taiwan has been promoting GBM labeling for nearly 10 years. The GBM labeling system has four main categories (ecological, healthy, high performance and recycling) and concerns about environmental toxicity and the utility of energy and resources (Chiang et al., 2009). In addition to GBM evaluation, this research applied the Eco-Efficiency Method (eco-efficiency=quality/load) (Ehrenfeld, 2005) in comprehensively considering the efficiency of GBM.

As defined by the World Business Council for Sustainable Development (Lehni, 2000), “Eco-efficiency is achieved by the delivery of competitively priced goods and services that satisfy human needs and bring quality of life, while progressively reducing ecological impacts and resource intensity throughout the life-cycle to a level at least in line with the Earth's estimated carrying capacity.” In short, eco-efficiency involves creating more value with less harmful effects (Stigson, 2000). This research applied eco-efficiency theory to further understand the relationship between GBM quality and environmental load to explore the ecological effectiveness of promoting GBM in Taiwan.

The research was performed in the following steps. First, we reviewed a number of GBM-related international sustainability evaluation systems and summarized literature about GBM evaluation factors (Table 1). The life-cycle assessment of ISO14040 was used as the summary basis to generalize GBM evaluation factors for developing the Initial GBM Evaluation System (Fig. 1). Second, we conducted a two-stage analysis and screen using the fuzzy Delphi method (FDM). The first stage involved an analysis of the evaluation indicators' eco-efficiency dimension (quality or load) and the importance of the evaluation factors. In the second stage, a questionnaire concerning the modified evaluation factors was used to analyze the importance of the evaluation factors for establishing the Taiwan GBM Eco-Efficiency Evaluation System. Third, we analyzed the priority vector of the evaluation indicators using the analytic hierarchy process (AHP) method before finally achieving the goal of constructing the GBM Eco-Efficiency Model. Therefore, the purposes of this research are providing an appropriate GBM evaluation method and the enhancement of Taiwan's GBM eco-efficiency for responding positively to human health, global environment and sustainable development.

FIG. 1.

Initial Green Building Material (GBM) Evaluation System.

Table 1.

Literature Review Pertaining To Green Building Material Evaluation Indicators

				Life cycle—cradle to grave
	Nation	Ref.	Green building material assessment system	Resource utilization	Energy utilization	Greenhouse gas emission	High-performance	Eco-friendly	Economic
International sustainability assessment system	International	a	ISO Guide 64	◯	◯	◯	◯	◯	◯
	United States	b	Leadership in Energy and Environment Design (LEED)	◯			◯
	International	c	SBTool 2012Sustainable Building Tool (iiSBE)			◯
	United Kingdom	d	Building Research Establishment Environmental Assessment Method (BREEAM)	◯		◯	◯
	Japan	e	Comprehensive Assessment System for Build Environment Efficiency (CASBEE)	◯				◯
	Sweden	f	Environmental Product Declaration (EPD)	◯	◯	◯	◯	◯
	United Kingdom	g	PAs 2050 (BSI)			◯
International green building material label	Germany	h	The Blue Angel(Eco-label with Brand Character)	◯			◯	◯	◯
	Nordic	i	Nordic Ecolabel(Nordic Ecolabelling Board)	◯		◯	◯	◯
	European Union	j	EU-flower(The European Union Ecolabelling Board)	◯	◯		◯	◯
	Japan	k	Eco-Mark(Japan Environment Association)	◯		◯	◯	◯	◯
	United States	l	Green Seal	◯			◯	◯
	Taiwan	m	Taiwan Green Building Material (TGBM)	◯	◯		◯
U.S. green building material research literature	United States	n	Building for Environmental and Economic Sustainability (BEES)		◯	◯	◯	◯	◯
	United States	o	Construction Specifications Instiute (CSI, U.S.A.)	◯	◯		◯	◯	◯
	United States	p	CalRecycle(California Department of Resources Recycling and Recovery)	◯			◯	◯

References: ^aISO/TC 207, 2008; ^bUSGBC, 2009; ^ciiSBE, 2012; ^dBRE Global, 2012; ^eJaGBC/JSBC, 2012; ^fInternational EPD System, 2012; ^gBSi, 2011; ^hEnvironmental Label Jury et al., 2012; ⁱNordic Council of Ministers, 2012; ^jEuropean Commission, 2012; ^kJEA, 2012; ^lGreen Seal, 2012; ^mABRI, 2011; ⁿLippiatt et al., 2010; ^oCSI, 2012; ^pCalRecycle, 2012.

◯, green building material (GBM) evaluation items.

Research Method

Eco-efficiency theory

Eco-efficiency has been proposed as one of the main tools to promote a transformation from unsustainable development to sustainable development (Guenster et al., 2011). Eco-efficiency is based on the concept of creating more goods and services, while using fewer resources and creating less waste and pollution. “It is measured as the ratio between the value of what has been produced and the environmental impacts of the product or service” (Yadong, 2013). The term was coined by WBCSD in the 1992 publication “Changing Course.” At the 1992 Earth Summit, eco-efficiency was endorsed as a new business concept and as a means for companies to implement Agenda 21 in the private sector (OECD, 2002). The term has become synonymous with a management philosophy that is geared toward sustainability through the combination of ecological and economic efficiency (BSD Global, 2013). The equation for eco-efficiency is as follows: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland,xspace}\usepackage{amsmath,amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6} \begin{document} \begin{align*} \hbox { Eco - Efficiency } = { \frac { \rm Quality \ ( Q_n ) } { \rm Load \ ( L_n ) } } \tag { 1 } \end{align*} \end{document}

The fuzzy Delphi method

The traditional Delphi method, which was developed by Dalkey and Helmer (1963), has been widely used to obtain a consistent flow of answers through the use of questionnaires (Hwang and Lin, 1987; Reza and Vassilis, 1988). The Delphi method is an expert opinion survey method with three features: anonymous response, iteration and controlled feedback, and statistical group response. Because people use words, such as “good” or “very good” to reflect their preferences, the concept of combining fuzzy set theory and Delphi became known as FDM. Noorderhaben (1995) indicated that applying FDM to group decisions can solve the fuzziness involved in reaching a common understanding of expert opinions.

This research applied triangular membership functions and fuzzy theory to solving the group decision. The FDM was used for the screening of alternate factors during the first stage. The efficiency and quality of questionnaires could be improved. Thus, more objective evaluation factors could be screened through the statistical results.

This investigation applied the method of Lee et al. (2006), who designed an Excel program based on the fuzzy Delphi operation model and the statistical software Excel Expert Choice 2000 to calculate relative values (Fig. 2). This research utilized the “bi-triangular fuzzy number” to identify the evaluation indicator, and then analyzed questionnaires completed by experts to analyze the importance of each of the evaluation items and to determine the most suitable GBM Eco-Efficiency Evaluation System.

FIG. 2.

Fuzzy Delphi method model. Cⁱ, conservative cognitive value; Gⁱ, expert consensus value; M_i, interval between geometric means of conservative and optimistic cognitive values; Oⁱ, optimistic cognitive value; Z_i, interval of overlap (gray zone) between minimum optimisitic and maximum conservative cognitive values; _L, minimum value; _M, geometric mean; _U, maximum value.

Analytic hierarchy process

The AHP is a structured technique for organizing and analyzing complex decisions (Saaty, 1980). Based on mathematics and psychology, this process was developed by Thomas L. Saaty in the 1970s. The AHP is particularly applicable to group decision making (Saaty and Peniwati, 2008) and is used around the world in a wide variety of decision situations. Rather than prescribing a “correct” decision (Bhushan and Kanwal, 2004), the AHP helps decision makers to identify the choice that optimally suits their goal and understanding of the issue. This process provides a comprehensive and rational framework for structuring a decision problem, representing and quantifying its elements, relating those elements to overall goals, and evaluating alternative solutions (Tiwari and Banerjee, 2001). The AHP is most useful in teams of people who are working on complex problems involving human perceptions and judgments and for resolutions that may have long-term repercussions. The use of the AHP has unique advantages when important elements of a decision are difficult to quantify or compare or when communication among team members is impeded by their different specializations, terminologies, or perspectives (Eddi and Hang, 2001). This research applied AHP to analyze the priority vector of the GBM evaluation indicators.

Review of the Literature on GBM Evaluation Factors

By summarizing international sustainability evaluation systems (ISO Guide 64, LEED, SBTool, BREEAM, CASBEE, EPD, and PAs 2050), international GBM labels (The Blue Angel, Nordic Ecolabel, EU-flower, Eco-Mark, Green Seal, and Taiwan GBM [TGBM]), and relevant building material evaluation mechanisms (BEES, CSI, and CalRecycle), we found that 37 evaluation items are used to evaluate GBM (Table 1) (Braat, 1991; ISO/TC 207, 2008; USGBC, 2009; Lippiatt et al., 2010; ABRI, 2011; BSi, 2011; BRE Global, 2012; CalRecycle, 2012; CSI, 2012; Environmental Label Jury et al., 2012; European Commission, 2012; Green Seal, 2012; iiSBE, 2012; International EPD System, 2012; JaGBC/JSBC, 2012; JEA, 2012; Nordic Council of Ministers, 2012).

After a methodical synthesis of the literature using the life-cycle assessment process (from raw material extraction through material processing, manufacture, distribution, use, repair and maintenance, and disposal or recycling) (Malin, 2010), the 37 evaluation items were integrated with 13 evaluation factors and 6 evaluation indicators.

The 6 evaluation indicators are resource utilization (R), energy utilization (E), greenhouse gas emission (G), high performance (H), eco-friendly (F), and economic (V), and they provide the basis for establishing the Initial GBM Eco-Efficiency Evaluation System (Fig. 1).

Empirical Research and Analysis

The importance of the evaluation items

First survey

Expert questionnaire: This research utilizes the FDM to design an expert questionnaire and appraises the relative importance of criteria in the form of numbers based on the fundamental scale of the FDM. This procedure was adopted in the questionnaire distribution phase to avoid any ambiguity or difficult readability issues that may affect the external nature of answers provided by the interviewees and to facilitate understanding.

Reza and Vassilis (1988) noted that the number of experts interviewed should be relatively limited and that interviewing 5–15 individuals is generally optimal. In addition, the expert consensus threshold (Gⁱ) should be greater than 5; therefore, this research set the expert consensus threshold (Gⁱ) greater than 6.

The first survey was conducted by integrating the opinions of experts from various fields using the FDM to cover the dimensions of eco-efficiency. The objective of the questionnaire was to query experts in the areas of building, building materials, equipment, energy, and eco-environment. Of a total of 18 questionnaires, 17 valid responses were received (response rate of 94.4%).

The results of the first survey:

The dimension of the GBM eco-efficiency evaluation indicator: The results were grouped into two categories. The first, GBM environmental load (L_n), included resource utilization (R), energy utilization (E), greenhouse gas emission (G), and eco-friendly (F)—4 evaluation indicators. The second, GBM quality (Q_n), comprised high performance (H) and economic (V)—2 evaluation indicators. The evaluation indicators and factors are summarized in Table 2.

1. Resource utilization: The highest expert consensus score was R-5-1 “Low-poison processing” (Gⁱ=8.68). The minimum expert consensus value was R-1-3 “Use natural resources” (Gⁱ=7.45>6, expert consensus threshold; Table 3 and Figs. 3 –12).

2. Energy utilization: The highest expert consensus value was E-1-1 “Increase the use of perpetual resources” (Gⁱ=7.88). The minimum expert consensus value was E-4-1 “Materials for energy recovery” (Gⁱ=7.38>6, expert consensus threshold; Table 4 and Figs. 13 –17).

3. Greenhouse gas emission: The highest expert consensus value was G-1-2 “Emission of greenhouse gases during manufacture” (Gⁱ=9.18). The minimum expert consensus value was G-1-4 “Emission of greenhouse gases during consumer use” (Gⁱ=7.41>6, expert consensus threshold; Table 5 and Figs. 18 –22).

4. High performance: The highest expert consensus value was H-1-5 “Building material performance in upgrading indoor air environmental quality” (Gⁱ=8.52). The minimum expert consensus value was H-1-6 “Upgrade factor (building material's performance)” (Gⁱ=7.39>6, expert consensus threshold; Table 6 and Figs. 23 –28).

5. Eco-friendly: The highest expert consensus value was F-1-3 “Hazardous waste disposal” (Gⁱ=9.16). The minimum expert consensus value was F-1-5 “Packaging” (Gⁱ=6.77>6, expert consensus threshold; Table 7 and Figs. 29 –33).

6. Economic: The highest expert consensus value was V-1-5 “Wearing parts and components for repair and replacement” (Gⁱ=7.73). The minimum expert consensus value is V-1-4 “Customer satisfaction” (Gⁱ=6.84>6, expert consensus threshold; Table 8 and Figs. 34 –39).

FIG. 3.

Importance of evaluation factors (R-1-1).

FIG. 4.

Importance of evaluation factors (R-1-2).

FIG. 5.

Importance of evaluation factors (R-1-3).

FIG. 6.

Importance of evaluation factors (R-1-4).

FIG. 7.

Importance of evaluation factors (R-2-1).

FIG. 8.

Importance of evaluation factors (R-2-2).

FIG. 9.

Importance of evaluation factors (R-2-3).

FIG. 10.

Importance of evaluation factors (R-3-1).

FIG. 11.

Importance of evaluation factors (R-4-1).

FIG. 12.

Importance of evaluation factors (R-5-1).

FIG. 13.

Importance of evaluation factors (E-1-1).

FIG. 14.

Importance of evaluation factors (E-2-1).

FIG. 15.

Importance of evaluation factors (E-3-1).

FIG. 16.

Importance of evaluation factors (E-3-2).

FIG. 17.

Importance of evaluation factors (E-4-1).

FIG. 18.

Importance of evaluation factors (G-1-1).

FIG. 19.

Importance of evaluation factors (G-1-2).

FIG. 20.

Importance of evaluation factors (G-1-3).

FIG. 21.

Importance of evaluation factors (G-1-4).

FIG. 22.

Importance of evaluation factors (G-1-5).

FIG. 23.

Importance of evaluation factors (H-1-1).

FIG. 24.

Importance of evaluation factors (H-1-2).

FIG. 25.

Importance of evaluation factors (H-1-3).

FIG. 26.

Importance of evaluation factors (H-1-4).

FIG. 27.

Importance of evaluation factors (H-1-5).

FIG. 28.

Importance of evaluation factors (H-1-6).

FIG. 29.

Importance of evaluation factors (F-1-1).

FIG. 30.

Importance of evaluation factors (F-1-2).

FIG. 31.

Importance of evaluation factors (F-1-3).

FIG. 32.

Importance of evaluation factors (F-1-4).

FIG. 33.

Importance of evaluation factors (F-1-5).

FIG. 34.

Importance of evaluation factors (V-1-1).

FIG. 35.

Importance of evaluation factors (V-1-2).

FIG. 36.

Importance of evaluation factors (V-1-3).

FIG. 37.

Importance of evaluation factors (V-1-4).

FIG. 38.

Importance of evaluation factors (V-1-5).

FIG. 39.

Importance of evaluation factors (V-1-6).

Table 2.

Dimension of Green Building Material Eco-Efficiency

		GBM eco-efficiency
Evaluation indicator	Evaluation factor	GBM quality (Q_n)	GBM load (L_n)
Resource utilization	R-1 Ecological material	5.89%	94.11%
	R-2 Renewable material	11.77%	88.23%
	R-3 Sustainable material	23.53%	76.47%
	R-4 Pollutant content limit	35.3%	64.70%
	R-5 Harmful substance content limit	41.18%	58.82%
Energy utilization	E-1 Perpetual resources utilization	11.77%	88.23%
	E-2 Nonrenewable resources	11.77%	88.23%
	E-3 Energy efficiency	11.77%	82.35%
	E-4 Energy recovery	29.42%	70.58%
Greenhouse gas emission	G-1 Greenhouse gas emission of building material	11.77%	88.23%
High-performance	H-1 High-performance-upgrade factor	88.23%	11.77%
Eco-friendly	F-1 Eco-friendly	11.77%	88.23%
Economic	V-1 Value/economic	76.47%	23.53%

Boldface indicates the more important dimension of each evaluation factor.

Table 3.

Importance of Evaluation Factors for Resource Utilization

		Most conservative cognitive value		Most optimistic cognitive value		Geometric mean
Evaluation factor	Evaluation item	Cⁱ_L	Cⁱ_U	Oⁱ_L	Oⁱ_U	Cⁱ_M	Oⁱ_M	Test M_i−Z_i	Expert consensus value Gⁱ
R-1. Ecological material	R-1-1. No shortage crisis	5	8	8	10	6.29	9.22	2.93	7.75
	R-1-2. Local materials	3	8	7	10	5.66	9.29	2.63	7.49
	R-1-3. Use natural resources	4	8	7	10	5.74	8.81	2.07	7.45
	R-1-4. Less labor treatment involved	4	8	7	10	6.33	8.96	1.63	7.54
R-2. Renewable material	R-2-1. Use of recycled materials	4	8	7	10	6.10	9.08	1.98	7.52
	R-2-2. Reducing usage of nonrenewable resources	5	8	8	10	6.48	9.27	2.79	7.87
	R-2-3. Components and materials for reuse	4	9	7	10	6.46	9.00	0.54	7.88
R-3. Sustainable material	R-3-1. Sustainable materials certification	4	8	8	10	6.23	9.18	2.95	7.71
R-4. Pollutant content limit	R-4-1. Avoid material containing pollutant	6	9	9	10	7.46	9.70	2.24	8.58
R-5. Harmful substance content limit	R-5-1. Low poisoned processing	7	9	9	10	7.58	9.79	2.22	8.68