Innovative product design method for low-carbon footprint based on multi-layer carbon footprint information

Journal of Cleaner Production 228 (2019) 729e745

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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Innovative product design method for low-carbon footprint based on multi-layer carbon footprint information Jun Peng, Wenqiang Li*, Yan Li, Yuanming Xie, Zilin Xu School of Manufacturing Science and Engineering, Sichuan University, Chengdu, 610065, PR China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 9 June 2018 Received in revised form 18 April 2019 Accepted 19 April 2019 Available online 23 April 2019

At present, analysis and design methods for a product’s carbon footprint are focused mostly on its existing structures, which are difficult to integrate with the conceptual design process of low-carbon, innovative products. To solve this problem, this study proposes a new low-carbon design method based on a multi-layer carbon footprint information model. The hierarchical carbon footprint information model is the combination of direct structural design elements with indirect design elements such as functions and principles. The study also proposes a method for qualitative/semi-quantitative carbon footprint calculation. To achieve this, design information is combined with product structure tree (PST) to form a greenhouse gas (GHG)-product structure tree (G-PST) based on the carbon footprint design information. The influence degree (Id) of each design element in the G-PST is evaluated by the analytical network process (ANP) method and the optimizable degree (Od) of each design element for low-carbon design is obtained. The low-carbon product design is divided into four categoriesdstructure optimization design, principle optimization design, function optimization design, and process optimization designdand the corresponding innovative design strategies are proposed. The results of this research can effectively obtain and standardize various low-carbon design elements, and provide targeted methods and tools for guiding designers to implement innovative low-carbon designs. The effectiveness of this low-carbon design method proposed is then tested on a Haier automatic washing machine, EB72M2JD. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Carbon footprint Design information Low-carbon design Innovative design Multi-layer

1. Introduction In the 21st century, global warming accelerated by greenhouse gas (GHG) emissions, particularly carbon dioxide, presents a crisis for the environment and the human society (IPCC, 2007). Product design with low-carbon footprint has become the focus of scientific research and industrial manufacturing since the signing of the Kyoto Protocol (Su et al., 2012; Xu et al., 2015). The Protocol, which includes a series of specifications such as ISO(14040/44/64/65/66/ 67) and PSA(2050/60), aims to regulate carbon emissions from products (Su et al., 2012; Xu et al., 2015) and mandates companies and designers to modify products to meet its low-carbon requirements (Dunbing Tang, 2017). Research shows that approximately 84% of GHG is derived from carbon emissions from energy generation (Park et al., 2009), and that electromechanical products are one of the major energy consumers. Therefore, low-carbon

* Corresponding author. E-mail address: [email protected] (W. Li). https://doi.org/10.1016/j.jclepro.2019.04.255 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

design of electromechanical products focused on researching the product’s carbon footprint and developing low-carbon design strategies, is an effective way of reducing carbon emissions (Song and Lee, 2010; Toby et al., 2010). There are two basic challenges confronting the design of lowcarbon footprint products e establishing a model to calculate and evaluate carbon footprint quantitatively; and implementing lowcarbon design strategies to reduce the carbon footprint (He and Hua, 2017; Zhang et al., 2012). For the first challenge, carbon footprint is widely used as an indicator to describe the environmental performance of a product. It is the sum of GHG emissions in the product life cycle, which is calculated in terms of CO2 equivalent (Toby et al., 2010). Since GHG emission data can be used to identify the key areas of improvement (Song and Lee, 2010), carbon footprint calculation is used as the basis for low-carbon product design (He et al., 2015a,b; Zhang et al., 2012). For the manufacturing stage, Jack C. P. Su et al. and Kan Fang et al. proposed some optimization algorithms based on multiobjective issues, including carbon emissions, to evaluate the

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carbon footprint and obtain its minimum value (Fang et al., 2011; Su et al., 2012). Jeswiet proposed a carbon footprint calculation method based on power consumption during product processing (Jeswiet and Kara, 2008). In the use stage, Qi Lu proposed a carbon emission calculation method based on temperature field, and analyzed the key factors contributing to high carbon units for supporting a low-carbon design (Lu et al., 2017). In addition, Antonio Scipioni created a model for monitoring and managing GHG emissions over the entire product life cycle, by integrating ISO14040 and ISO14064 (Scipioni et al., 2012). Elhedhli proposed a carbon footprint model for the supply chain based on Lagrangian relaxation (Elhedhli and Merrick, 2012). Tsai ChiKuo created a carbon footprint assessment framework based on the supply chain to help companies expeditiously calculate the product’s carbon footprint (Kuo, 2013). As for the expression of product information in a low-carbon footprint design, its traditional model is based on function-behaviorstructure (FBS) (Gero, 1990). For example, Bin He constructed a feature-based integrated product model, and he defined these features as a collection of important information elements that is related to the low-carbon design process (He and Hua, 2017). However, these models did not fully consider the expression of carbon footprint-related information. Besides, they mostly acted as a barrier between information expression and low-carbon design. It can be observed that the existing expression and calculation methods are mainly based on life cycle assessment (LCA), and the core idea of these methods is similar to that of ISO14067 or PAS2050, in which the carbon footprint is calculated based on activity data from the product’s life cycle (Institution, B.S., 2011). For the second challenge, designing a low-carbon product must consider the GHG emissions over the entire life cycle of that product and product innovation with reduction in the carbon footprint. The result is a low-carbon product that satisfies the carbon emission regulations. Jong-Sung Song devised a low-carbon design system that integrates the GHG emission data of components into the bill of material (BOM), to mark high-carbon footprint structures throughout the product life cycle, and then find alternatives to those structures (Song and Lee, 2010). Yu QI proposed an innovative low-carbon strategy based on a modular design (Yu and WU, 2011). In another study, Xian-Chun Tan developed a method for determining production processes that are centered on low-carbon production (Tan et al., 2011). Cheng Zhang proposed an approach that integrates structural optimization and material selection to reduce carbon emissions from mechanical parts (Zhang et al., 2018). Bin He in their studies regarded low-carbon footprint requirement as a design constraint and sought a combination of low-carbon elements in the product life cycle. They conceptualized the low-carbon design process as the search for the design solution with the lowest carbon footprint (He et al., 2015a,b). Zhang Yi proposed a double progressive positioning method based on product structure tree and detailed design parameters to locate the core feature of carbon footprint. Then, according to the contradiction of design parameters, the theory of inventive problem solving (TIPS or TRIZ) was used for improving the structural design (Yi et al., 2017). Based on the aforementioned eco-design constraints, Alexandre Popoff studied the impact of changes in design parameters on the overall performance of a product’s life cycle, and then they used the quality function deployment (QFD) to create low-carbon models of products that met consumer needs (Alexandre Popoff, 2017). Bao Hong arrived at an improved low-carbon design based on the static and uncertain analysis of a product’s carbon footprint. They quantified and assigned the carbon footprint units and analyzed the sensitivity of the design parameters to the carbon footprint (Bao Hong, 2013). Xu Feng introduced a carbon footprint calculation model into the conceptual design phase and developed a corresponding method with low-carbon constraint based on the QFD principle

(Feng et al., 2013). Though the research works mentioned above play a guiding role in development of low-carbon product design, they also have limitations: (1) The carbon footprint calculation methods based on activity data of a product life cycle belong to ex-post calculation. The process is too complex to use in product design (Lu et al., 2018; Zhang et al., 2012), resulting in a disconnect between carbon footprint calculations and low-carbon designs. (2) One of the important tasks of low-carbon product design is to identify indirect elements such as high-carbon targets (Lu et al., 2018; Song and Lee, 2010). However, existing studies mainly identified only direct elements related to carbon footprint, such as structures or structural parameters with high-carbon footprint. They have not carried out research on low-carbon structures considering indirect factors, such as functions, principles, etc., which impact carbon footprint of the final product. Besides, few literatures have described sensitivity analysis tests on structural parameters after identifying the high-carbon targets (de Koning et al., 2009; Hongcheng Li, 2012). These deficiencies have led to the lack of comprehensiveness of research on low-carbon design. (3) The major strategies that exist today obtain low-carbon designs by selecting alternatives of high-carbon structures or comparing multiple design choices in the life cycle, which is devoid of innovation and cannot act as a special guide for the design process. To solve the abovementioned problems, this research aims to conduct a comprehensive study of the product’s carbon footprint, from both direct and indirect elements, propose a low-carbon design process with operability from the perspective of innovation, and provide corresponding low-carbon design strategies for different elements. The paper is organized as follows: (1) in section 2.1, the product model of a multi-layer carbon footprint is proposed to fully express the carbon footprint in all aspects and study the relationship between them. (2) In section 2.2, methods for calculating and expressing carbon footprints are provided. The calculation is based on design information. A relationship tree, called GHG-PST is proposed to allow designs to evaluate different elements. (3) In section 2.3, two concepts, influence degree (Id) and optimizable degree (Od) of design elements, for carbon footprint evaluation are proposed to delineate the core elements in both direct and indirect sides, estimated by the analytic network process (ANP). (4) In section 2.4, four types of low-carbon design strategies are studied from the perspective of innovation. (5) Finally, in section 3, a case study that verifies the feasibility of this study and the proposed method are presented. The research presented in this paper is a follow-up to our previous work. The differences between the two are: C the focus of the former study was on direct design elements, while this work studies the carbon footprint of the product from both direct and indirect elements, including structure, function, prinicle, and process. C the previous study only examined the impact of elements on the carbon footprint of a product and used it to determine the core elements. However, this work further studied the relative merits of the feasibility and ability of the core elements to reduce the carbon footprint. C the existing study did not take into account product innovation when acquiring low-carbon design strategies, while this study proposed four types of design methods from the perspective of an innovative low-carbon design.

J. Peng et al. / Journal of Cleaner Production 228 (2019) 729e745

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2. Materials and methods

footprint and the elements in each layer.

2.1. Product model of multi-layer carbon footprint expression

GCF fDI

For low-carbon design, a product information expression model centered on carbon footprint must be established. In this study, the product’s design information is defined as a collection of important design elements associated with carbon footprint of the product’s life cycle. For this study, structures have been considered as the main body of design information, which is divided into direct elements (DE) and indirect elements (IE) according to the influence their characteristics have on carbon footprint. Among these elements, DE produce carbon emissions directly or have a direct impact on carbon emission activities. IE impact carbon footprint indirectly. DE are divided into structure elements (SE) and process elements (PE). The former includes engineering parameters such as dimensions, weights, and precisions, as well as materials and packaging, etc., which are attributes of the structure or are determined by it. The latter refers to the factors associated with carbon emissions during processing, transportation, usage, and recycling of the product, including processing methods, transportation tools, transportation distances, usage patterns, service life, maintenance methods, recovery rates, and so on. As for IE, they do not directly produce carbon emissions, but they can affect or determine a product’s carbon footprint objectively. Such elements include functions (F) and principles (P). For example, a washing machine’s function of stirring does not directly produce carbon emissions; however, the structure and energy consumption of the agitator does. Moreover, the principle of agitation does not directly produce carbon emissions, but different principles lead to the washing machine leaving different levels of carbon footprints. Because a function is implemented by several actions (A), and each action corresponds to an interrelated principle (Pahl and Beitz, 1996), there is a hierarchal structure of functionaction-principle involved. In summary, the design information of a structure can be expressed as follows:

2.2. Calculation and expression of carbon footprint

DIi ¼ fðSE1; SE2; …Þ; ðPE1; PE2; …Þ; ðF1; F2; …Þ; ðP1; P2; …Þg (1) where DIi is design information of the ith structure, SE is structure element, PE is process element, F is function, and P is principle of each action. A model of the carbon footprint-structure-design (C-S-D) information, as shown in Fig. 1, was built. Based on the assembly relationship, the product structures are divided into four levels. Level 0 is the product, level 1 constitutes of the subsystems of the product, level 2 the modules of each subsystem, and level 3 the submodules of each module. This kind of level-by-level deployment forms the PST. Considering the research purpose, however, this approach is of little significance and will only increase the workload required to study the functions and principles in deeper levels. Therefore, the PST has been expanded only to the submodule level in this study. The structures at each level correspond to their respective DE and IE design information. According to ISO14040, the life cycle of a product is divided into five stages (ISO, 2006): material acquisition (stage S1), manufacturing and assembly (stage S2), transportation (stage S3), usage (stage S4), and recycling (stage S5), which are the calculation boundaries of the carbon footprint. The carbon emission factors used in the calculations are taken from database. Because each design element influences the carbon footprint of the product, the carbon footprint data of a structure can be associated with its design information if it is available (Song and Lee, 2010), i.e., there is a mapping relationship between carbon

(2)

2.2.1. Carbon footprint calculation based on design information In any case, calculation of carbon footprint is important. However, it is difficult to collect activity data due to the uncertainty of process elements. DE are the root cause of carbon emission activities, and Toby Joyce pointed out the key parameters of electromechanical products that determine approximately 90% of their carbon emissions (Toby et al., 2010). Therefore, a simplified method for calculating carbon footprint was proposed based on design information, which is as follows: the process elements of the product are standardized, which is known as life cycle designing. The design information should be sorted out by designers in the design stage. For example, the standardized design of service life, usage, and recovery rate should be conducted just like it was for the design of structure elements, to obtain a theoretical list of direct elements. Combining this information with database, the product carbon footprint can be calculated qualitatively/semi-quantitatively. The calculation process and formulas are as follows: S1: At this stage, the carbon emissions resulting from GHG emissions generated during production of materials for structures. The effect of offsetting on the carbon footprint of material recovery, in the recycling stage, should be considered.

GS1 ¼

n Xh X    i Mij $dMij $ 1  xij þ Mip $ dMip $ 1  xip

(3)

i¼1 j;p

Where Mij is the consumption of the jth material for the ith structure, Mip is the quantity of packaging material for the ith structure, dMij and dMip are the carbon emission factors of the structural material and the packaging material, respectively, (in kgCO2$kg1), and xij and xip are the recovery rates of the corresponding materials. S2: Carbon emissions from this stage mainly come from the direct GHG and energy consumption of the processing, and the carbon footprint caused by assembly process is included in the calculation in the form of impact factors.

GS2 ¼

n X X i¼1

Eij $dEj $ð1 þ yi Þ þ Gij þ Wij $dWj



(4)

j

where Eij is the theoretical enertgy needed in the jth processing process under the designed processing method of the ith structure, dEj is the carbon emission factor for producing the energy (in kgCO2$J1), yi is the assembly factor for the ith structure, Gij is the direct GHG emission of the ith structure in the jth processing process, estimated by designers based on the processing method, database’s information and designer’s own experience etc., Wij is quantity of waste produced in the jth processing, and dWj is emission factor of the waste. S3: At this stage, the carbon footprint mainly owes to the energy consumed by transportation tools. The calculation needs to consider transportation during the recovery stage, which is calculated in the form of impact factors.

GS3 ¼

n X X Mi $dij $dij $ð1 þ zi Þ i¼1

(5)

j

where Mi is the quantity of transport object, including materials, parts and products, dij is transport distance in the jth transportation under the designed transport mode, dij is the carbon emission

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PST

Indirect element Layer

Lever 0 (Product)

...

Design information

Lever 1 (System)

...

Principle

...

Lever 2 Module

Action

Principle

...

...

Function

Action

...

...

Lever 3 Submodule

...

Direct element Layer

Lever 3 Submodule Engineering parameters

Weights

Dimensions

...

Precisions

Processing technology

Transportation tools

Usage patterns

Recovery rates

Energy consumption

Transportation distances

Energy consumption

Waste treatment

Assembly

...

Maintenance

Recycling transportation

...

...

Transportation

Using

Recycling

...

...

... Materials

Lever 1 (System)

Processing

Packaging

·

...

Carbon footprint calculation

...

Life cycle layer

Life cycle designing Database

S1

Carbon footprint Structure Element

Structure

S2 Process Element

S3

S4 Action

S5 Principle

Life cycle stage

Fig. 1. Product model of carbon footprint-structure-design information flow.

factor of the corresponding transportation, whose unit is kgCO2$(kg$km)1, and zi is the additional factor affecting transport that considers recycling. S4: At this stage, carbon emissions mainly arise from the electric energy consumed by the electromechanical product in standard usage and maintenance modes.

GS4

20 1 3 n X X 4 @ A ¼ Pij $tj þ Em $dEF þ G4i 5 i¼1

(6)

j

where Pij is the electric power of the ith structure in the jth standard usage mode, tj is the usage time of the corresponding usage mode within service life, Em is the electric power consumed for maintenance, dEF is the equivalent electric power emission factor whose value is 0.424 kgCO2/kwh (Song and Lee, 2010), and G4i is the estimated value of the direct GHG that may be produced during usage. S5: At this stage, carbon emissions mainly arise from the waste disposal and material recovery process, and are calculated according to the designed recycling method, among which materials recovery and its transportation have been considered in the previous stages.

GS5 ¼

n Xh i X  Mrij $dj þ Ej $dEj i¼1

(7)

j

where Mrij is the amount of waste disposal of the ith structure in the

jth disposal process, dj is the carbon emission factor of the corresponding disposal process (in kgCO2$kg1), Ej is the energy consumed accordingly, and dEj is the carbon emission factor caused by producing energy (in kgCO2$J1).

GCF ¼ GS1 þ GS2 þ GS3 þ GS4 þ GS5

(8)

It should be noted that the result of the carbon footprint calculation is not the actual footprint of the product, but rather a relatively accurate reflection of the distribution of the product’s carbon footprint. Since the key parameters determine about 90% of carbon emissions, the reflection has strong confidence. In addition, the purpose of this study is to devise a low-carbon design for the product rather than conducting a full-scale research on the carbon footprint left by the product. Apparently, this is also the main difference between the carbon footprint calculation method proposed in this article and those in ISO14067 or PAS2050. The core of the calculation method in this study is the standardized design of life cycle, which makes it unnecessary needs to collect complex activity data. Thus, designers can quickly evaluate the carbon footprint of products, even a conceptual product, which is of great significance to low-carbon product design.

2.2.2. GHG-product structure tree For recording carbon footprint and fully expressing product information, this study combines the BOM, design information, and carbon footprint data are combined with PST to form a G-PST. As

J. Peng et al. / Journal of Cleaner Production 228 (2019) 729e745

BOM

Product

Design Information

Number

Name

Amount

100

name

1

...

...

...

110

name

1

...

...

Submodule

111

name

2

...

Submodule

112

name

3

...

System

Module

DE

IE

S2

S3

S4

S5

...

GS1

GS2

GS3

GS4

GS5

...

...

GS1

GS2

GS3

GS4

GS5

...

...

...

GS1

GS2

GS3

GS4

GS5

...

...

...

GS1

GS2

GS3

GS4

GS5

...

...

...

...

...

...

...

...

...

...

GS1

GS2

GS3

GS4

GS5

...

...

...

...

...

...

...

...

...

...

...

...

...

200

name

2

...

...

...

...

Module

Structure

...

Structure Elements

...

...

Carbon Footprint S1

...

...

The relationship between DI&CF

733

Essential design elements

...

...

...

...

c Process Elements

...

Actions

...

Principles

Optional in the figure

Fig. 2. GHG-product structure tree.

shown in Fig. 2, the BOM includes information such as the number, name, and quantity of the structure, Design information is the collection of direct and indirect elements, carbon footprint information records the distribution of carbon footprints of structures in the five stages. In consideration of the following study, the relationship between design information and carbon footprint could also be recorded in the G-PST, to fully express the product around its carbon footprint. This kind of expression method for a product’s carbon-footprint information built on PST has a strong structural reflection, i.e., It can clearly reflect the corresponding relationship among structures, design information, and carbon footprint.

2.3. The relationship between design information and carbon footprint The C-S-D model constructed above is characterized by structures as the main body, which connects carbon footprint and design information. The proposed qualitative/semi-quantitative calculation method could allow designers to quickly assess a product’s carbon footprint. However, only obtaining high-carbon structures and high-carbon stages will not suffice for the design of low-carbon products. In this work, which emphasizes the role of carbon footprint data, further studies on the relationship between design elements and carbon footprint are carried out. Therefore, for comprehensively guiding product low-carbon design, the concepts of influence degree (Id) and optimizable degree (Od) of design elements on carbon footprint are proposed to obtain the essential elements in both DE and IE.

2.3.1. Influence degree The carbon footprint of a product depends on its structures and that of a structure depends on its design elements. Therefore, a product’s carbon footprint is ultimately decided by the design elements. In this study, the percentage impact each element has on the carbon footprint of a product is defined as its carbon influence degree. This degree is further divided into the influence degree of direct element (IdD) and the influence degree of indirect element (IdI). Both IdD and IdI reflect the mapping relationship between the structure’s carbon footprint and design elements, but from different information layers. The mapping assigns the carbon footprint of the upper-layer element (U-e) to their lower-layer elements (L-e) in a certain proportion. As shown in Fig. 3, the footprint is divided into three layers of maps: structure to direct elements, structure to actions, and action to principles. The distribution ratio of an upper element’s carbon footprint to its L-e is called the importance degree (Imd) of the L-e to the U-e. Therefore, the Id of a design element is equal to its Imd multiplied by the Id of its U-e. As shown in formulas (9)e(12), the Id of a direct element is the result of distribution in its own structure; the Id of a function is the sum of distributions of each structure’s carbon footprint over all its actions; and the Id of a principle is the sum of distributions that each action’s to it. Design elements with an Id that exceeds a certain value are called high-carbon elements, which are divided into highcarbon direct elements, high-carbon functions, and high-carbon principles.where Idsi is the Id of the ith structure; IdDit is the Id of the tth direct element of the ith structure; IdFk is the Id of the kth function; IdPh is the Id of the hth principle; uit is the Imd of the tth direct element to the ith structure; uikm is the Imd of the mth action

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Influence degree of indirect elements Idp1

Principle

n Function

Idp2

IdA1

Id dA2

...

IdD1

...

IdD2

...

IdA3

Idsi

IdD3

Idph

IdAm

...

IdD4

...

IdDt

Influence degree of direct elements Carbon footprint of structure

Carbon footprint of structure element

Carbon footprint of action

Carbon footprint of priniple

Carbon footprint of process element Function

Fig. 3. The mapping relationship of carbon footprint.

IdSi ¼

Gi  100% G

(9)

IdDit ¼ IdSi $ uit IdFk ¼

n X X

(10)

IdSi $uikm

(11)

i¼1 m

IdPh ¼

n X n X X

IdSi $uikm $umh

(12)

i¼1 k¼1 m

that belongs to the kth function to the ith structure; umh is the Imd of the hth principle to the mth action. 2.3.2. The calculation of importance degree based on ANP The ANP is a method used to obtain the relative priority of indicators through personal judgement or through normalization of actual measurement results into a relative form (Saaty, 2009). In this study, ANP was used to calculate the Imd of design elements. Specifically, the L-e are taken as comparison objects, and their influence on the U-e as comparison criteria. Then, judgement matrix A is formed by comparing pair-wise the L-e relative to the carbon

footprint of U-e, i.e., on a numerical scale of 1e9. The comparing value ranges from 1/9 for the “least valued than” to 1 for “equal”, and to 9 for “absolutely more important than”, covering the entire spectrum of comparison. As an example, DE such as materials, dimensions, precision, power, processing, packaging, transportation, usage, lifetime, and recovery rate of high-carbon structures are taken as comparison objects, whose Imd to the structure are numbered as u1,u2,...,un. The pair-wise comparison between them is then conducted by experts based on design information, carbon footprint calculation results, and experience. The judgment matrix is shown in formula

J. Peng et al. / Journal of Cleaner Production 228 (2019) 729e745

(13). Experts include multi-level personnel combination of design, processing, and management, who generate reasonable scale values as much as possible. After completing the judgment matrix, the consistency index, m, is used to judge whether the dominance comparison of each element is reasonable. In ANP, an inconsistency within 10% is acceptable. Finally, by solving the main eigenvector, u, of the judgment matrix, the Imd vector of each object is obtained.

u1 =u1 6 u2 =u1 6 A¼ 4 « un =u1 m≡

Goal: reduce the carbon footprint of high-carbon structures

Control layer

3

2

u1 =u2 … u1 =un u2 =u2 … u2 =un 7 7 1

…

«

Technical feasibility

(13)

5

(14)

n1

Direct element cluster

where lmax is the maximum eigenvalue of judgment matrix A.

2.3.3. Optimizable degree It is intuitive that high-carbon elements should have a major impact on a product’s carbon footprint throughout its life cycle; but how do designers decide whether to improve or change a highcarbon element? To answer this question, the Od of a highcarbon element to carbon footprint is proposed. This Od is defined as the relative merits of the feasibility and the ability to reduce carbon footprint by improving the high-carbon element, which is divided into optimizable degree of direct elements, functions and principles. The feasibility evaluation, termed feasible degree (Fd) in this study, is conducted from both engineering and cost perspectives. The greater the Fd is, the lower the technical requirements and costs of improving the high-carbon element are, and the greater the Od of the element is. For the ability evaluation, the sensitivity of each element to the carbon footprint is measured. Adding a basic increment, DDEit, to the tth direct element of the ith high-carbon structure in the design process, the corresponding variation of the product’s carbon footprint is DG. As shown in formula (15), the ratio of the two is the sensitivity degree (Sd). Considering that IE do not directly affect carbon footprint, to obtain the Sd of IE, the Sd of DE is allocated according to the mapping relationship and the Imd between S and IE, as shown in Eqs. 16 and 17. The greater the Sd is (the greater ability to reduce carbon footprint by optimizing the element), the greater the Od of the element is. Therefore, the Od of a high-carbon element x can be calculated as in Eq. (18).

DG DDEit

SdFk ¼

SdPh ¼

(15)

n XX X i¼1

t

Sdit $uikm

(16)

a

n XXX X Sdit $uikm $umh i¼1

Odx ¼ P

Cost

un =u2 … un =un

lmax  n

Sdit ¼

735

t

k

Principle cluster Function cluster

External Ext x ernal dependence

Internal dependence

Fig. 4. The structural network model of feasibility.

2.3.4. The calculation of feasible degree As shown in Fig. 4, a structural network model of feasibility of low-carbon improvement for design elements has been established, with technical feasibility and cost as the evaluation indices, and with the aim of reducing the carbon footprint of high-carbon structures. The layers of high-carbon element collections in direct elements, functions, and principles are called clusters. If one element of a cluster has relationships with at least two elements of another cluster, the former is said to have an external dependency on the latter. Besides, the elements in one cluster can also be interdependent. Using the ANP, the steps for calculating the Fd of each high-carbon element are established as follows: Step one, all the correlations in the model (i.e., external dependencies and internal dependencies) are compared in pairs under the evaluation index of technical feasibility and cost, respectively. For example, if an action in the function cluster has a correlation with two or more elements in the direct element cluster, then the action is regarded as a subcriterion. The judgment matrix formed by the pair-wise comparison in the DE is shown in Table 1, which can be used to calculate the weight vector. Step two, build a super-matrix. As shown in Eq. (19), each cluster is arranged by numbers C1, C2, and C3 on the behalf of clusters of direct elements, functions, and principles. Each cluster’s elements are vertically placed on the left side of the super-matrix and horizontally on the top of the matrix. The weight value u as the subcolumn of the corresponding column in the super-matrix is placed in its corresponding position in the matrix.

(17)

a

t ðSdit $Fdit Þ

þ

P

Sdx $Fdx

k ðSdFk $FdFk Þ

þ

P  h

Sdph $Fdph



(18)

where Sdit, SdFk, and SdPh are the Sd of the tth direct element, the kth function, and the hth principle, respectively, and Fdit, FdFk, and FdPh are the corresponding feasible degrees.

Table 1 The judgment matrix of an operation between elements in the direct element cluster.

DE1 DE2 … DEn

DE1

DE2

…

DEn

Weight value (u)

u1 =u1 u2 =u1

u1 =u2 u2 =u2

… … … …

u1 =un u2 =un

u1 u2

un =un

un

…

un =u1

…

un =u2

…

…

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J. Peng et al. / Journal of Cleaner Production 228 (2019) 729e745

C1 Wp ¼ C2

C3

C1 e11 e12 …e1n1

e11 … e1n1

C2 e21 e22 …e2n2

C3 e31 e32 …e3n3

2 4

e21 … e2n2

w12 w22 w23

w13 w23 w33

3 5

(19)

e31 … e3n3

2 6 6 wi1 ðj1 Þ 6 6 6 Wij ¼ 6 wi2 ðj1 Þ 6 « 6 6 4 wini ðj1 Þ

 

wi1 ðj2 Þ

…

wi2 ðj2 Þ 1

… …

wini ðj2 Þ

…

3

jn 7 wi1  j  7 7 7 jn wi2 j 7 7 i; j ¼ 1; 2; 3 «  7 7 7 jnj 5 wini

(20)

where Wp is the super-matrix constructed under the evaluation index p, and each column in Wij is a weight value obtained by a judgment matrix for comparing elements in the Ci cluster, whose subcriterion is the jn element in the j cluster. Step three, obtain a weighted super-matrix. Under each of the evaluation indices, the clusters and their connected clusters are compared by pairs to obtain the weight matrix of each cluster, which would be used to measure the significance of corresponding column blocks in the super-matrix. Then, a weighted super-matrix could be obtained by multiplying each column block, Wij, with the corresponding value of the Ci cluster in the weight matrix. Power the weighted super-matrix and let the power exponent approach infinity, until the elements of each column no longer change, then a limit super-matrix would be obtained. Step four, get the feasible degree of each element. Calculate the main eigenvectors of the limit super-matrix, which are the relative values of Fd under technical or cost perspectives, to improve all elements with the aim of reducing carbon footprint. As shown in Eq. (21), to obtain the final feasible degree, the weights of the two evaluation indices of technical feasibility and cost are comprehensively considered.

0

w11 w21 w31

uTD1

1

0

uCD1

1

B … C B … C B B C C B uTDn1 C B uCDn1 C B B C C B uT C B uC C F1 C B F1 C B B C C Fd ¼ ut $B B … C þ uc $ B … C B uT C B uC C B Fn2 C B Fn2 C B uT C B uC C B P1 C B P1 C @ … A @ … A

uTPn3

(21)

uCPn3

where ut and uc are the weights of the two evaluation indices with the goal of reducing carbon footprint, and their values are determined according to specific circumstances. 2.4. Innovative low-carbon product design strategies and processes The previous work is the foundation of an low-carbon

innovative product design. Especially, Id points out high-carbon elements, while Od is used to guide the design direction. Essential design elements are the ones that have both high Id and high Od according to the situation. As shown in Fig. 5, four low-carbon innovative design strategies are proposed for essential design elements: strategies for the product itself, which is divided into structure optimization design, function optimization design, and principle optimization design; product life cycle; and optimization strategy for manufacturing, transportation, usage, and recycling processes. Structure optimization design: Core structural elements affect carbon footprint by influencing weight, volume, recycling of the structure, etc.; therefore, the product involves lightweight design, design for disassembly and recycling, and selection of low-carbon materials. However, other contradictions often occur when meeting low-carbon requirements; for example, when a structure element has the opposite requirements in low-carbon design, physical conflicts arise. This study solves such problems with TRIZ. Function optimization design: Use “systematic inventive thinking (SIT)” for low-carbon innovative design of core function elements. According to the harmful parameter table, and the system and the surrounding environment table, core actions and related elements are combined, cropped, converted, or segmented using SIT’s reorganization strategy and expansion strategy. Principle optimization design: For core principle elements, with the help of some scientific effect libraries, high-carbon principles can be replaced according to functional requirements. Such a strategy even includes analysis of the original requirements to realize the demand in an entirely new way. Process optimization design: For low-carbon design in the manufacturing and transportation stages, process innovation, supply chain optimization, logistics optimization, etc., which belongs to low-carbon innovations in industrial technology and management technology, can be performed. Therefore, it is not discussed in detail. However, the core elements recognized in the two stages could be used as a reference for further innovation. For low-carbon design in the usage and recovery stages, a product service system is introduced. Aurich pointed out that a product service system design can reduce the damage to the environment (Aurich et al., 2006). Therefore, a low-carbon innovative design for the product service system was carried out. In addition to this, service functions would be used to supplement, coordinate, or substitute for product functions, which will reduce the physical carbon footprint. In the process of devising an innovative product design for lowcarbon footprint, the four strategies discussed above can be used jointly under specific circumstances. Summarizing the full extent of

J. Peng et al. / Journal of Cleaner Production 228 (2019) 729e745

737

Process optimization design

Structure optimization design Selecting lowcarbon materials

Lightweight design Design for disassembly

Manufacturing

Transportation

Process innovation

Supply chain

Use

Recovery

Supplementing product functions

optimization

... Design for recycling

Logistics optimization

...

Physical contradiction Technical contradiction Final ideal solution

Essential structure elements

Essential process elements

SubstitutIng product functions

Service

TRIZ

Coordinating product functions

system

High-carbon structures Combining Reorganization strategy

Essential functions

Essential priciples

Cropping

Retrieving principle

Segmenting Expansion strategy

Analyzing function requirements

Scientific effect libraries

SIT Converting Convert r ing

Function optimization design High-carbon structure element High-carbon process element

Design principles Low-carbon process element

Principle optimization design Low-carbon structure element

Service

Submethod

High-carbon principle

Low-carbon principle

High-carbon function Method

High-carbon structure Low-carbon structure

Low-carbon function Process

Fig. 5. Four innovative design types for low-carbon.

the research, the process planning of design for low-carbon footprint is shown in Fig. 6. In the first step, the design information at each layer is analyzed hierarchically to construct the C-S-D model. In the second step, the qualitative/semi-quantitative carbon footprint calculation based on the design information was conducted, and the carbon footprint, design information, etc. were combined with the PST to form the product’s G-PST map. The third step is to calculate the Id and Od of the elements of each layer, obtaining high-carbon elements through Id and key design elements through Od, which combines feasibility and sensitivity analyses. The fourth step is to implement the innovative design for low-carbon footprint on the product, propose innovative design solutions from different strategies and evaluate them, and then select qualified solutions to enter detailed designs.

3. Applications 3.1. Get GHG-Product structure tree A case study on a Haier automatic washing machine (model number EB72M2JD) with a maximum washing capacity is 7.2 kg and eight different levels of water volume was conducted. The washing power and the dehydration power are 415 W and 270 W, respectively. The total mass is 29 kg, with a size of 540  520  906 mm. The washing procedures include common use, disinfecting washing, surf washing, quick washing, washing, rinsing, and dehydration. The life span of the machine was assumed to be 10 years, 300 times per year, 30 min per washing, 2 min of dehydration. According to the model presented in section 2.1, the structures of the washing machine are divided into four levels, from which the design information at all the level’s structures could be

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Goal: low-carbon innovative design for products

The first step

Program evaluation

The fourth step

Low-carbon innovation strategies Get PST of the product

Database /Material

Analyzing design information hierarchically

Direct c elements

The second step

Structure Struct c ure optimization design

Process optimization design

Function optimization design

Principle optimization design

Core C ore structure elements

Core process elements

Core functions

Core principles

Indirect elements

Essential elements

Calculating Carbon footprint

Feasible degree

Sensitivity degree

Optimizable degree

Get G-PST of the product

High carbon structures

High carbon life f cycle

Whether to meet low carbon?

High carbon direct elements

The third step

High carbon indirect elementss indirect

Influence degree

Yes Qualified solutions

No

Detailed design

Flow direction arrow

Including relation

Process

Evaluating the impact of design elements on carbon footprint

Child content

Evaluative dimension

Design type

Fig. 6. The flow chart of innovative product design for low-carbon footprint.

obtained. For example, the design information of the pulsator includesdthe material is ABS plastic, the processing method is injection molding, the functions include toggling water flow friction and decontamination, the actions include rotation and clothing friction, the principles include rotational force and friction effect, etc. Referring to Eqs. (3)e(8) of subsection 2.2.1, the qualitative/ semi-quantitative carbon footprint calculation was performed for the structure of each level, in which the carbon footprints of the pulsator are: GS1 ¼ 0.73 kgCO2, GS2 ¼ 0.92 kgCO2, GS3 ¼ 0.09 kgCO2, GS4 ¼ 26.95 kgCO2, and GS5 ¼ 0.2 kgCO2, and the total amount of the footprint is 28.89 kgCO2. Finally, the G-PST of the washing machine is shown in Fig. 7. The high-carbon structures have washing and dewatering system (14.2%, level 1), transmission system (59.9%, level 1), and electric motor (52.8%, level 2), control and display panel (3.2%, level 2), pulsator (5.4%, level 3), belt mechanism (4.0%, level 3), etc.

3.2. Obtaining essential design elements In order to make full use of the carbon footprint data, the Id and Od of design elements of high-carbon structures were further calculated, and the results are shown in Fig. 8. In view of space limitations, only the pulsator is used as an example to demonstrate the calculation process, as follows:

(1) Id: As shown in Table 2, for the comparison between direct elements of the pulsator, an element with weight value greater than 10% is considered as being a high-carbon element of the pulsator, which includes the usage (42.8%), diameter (13.4%), and material (11.8%). In addition, in the function layer, there are toggling water flow (83.3%), friction and decontamination (16.7%), and in the principle layer, there are rotational force (83.3%) and friction effect (16.7%). The weight value of each element multiplied by the pulsator’s Id (5.4%) equals the element’s influence degree, for example, the diameter’s Id is 0.72%. The calculation method is detailed in subsection 2.3.2. 2. The calculation of Fd, taking the assessment of technical feasibility for high-carbon elements as an example: The matrix for direct elements compared to the function of toggling waterflow is shown in Table 4, where the matrix’s weight value is the subcolumn of the toggling waterflow column corresponds to each direct element in the super-matrix. As shown in Table 5, data in the (i, j) subblock reflects the influence of elements in cluster Ci on those in cluster Cj; for example, the bold (1, 2) block’s data in the table reflects the influence of the direct cluster’s elements on the function clusters. Under the evaluation index of technical feasibility, clusters are compared pair-wise, according to their connections; for example, the direct element cluster C1 has connections to C1, C2,

J. Peng et al. / Journal of Cleaner Production 228 (2019) 729e745

739

Fig. 7. EB72M2 washing machine’s G-PST.

and C3. Their pair-wise comparisons are shown in Table 6. This makes it possible to obtain the weight matrix, as shown in Table 7. The weights are then multiplied by the corresponding blocks in the super-matrix in Table 7. For example, the first weight value in the first row of Table 7 and the (1,1) block in Table 5 are multiplied, Finally, the weighted super-matrix was obtained as Table 8. The calculation of the limiting weight vector of this weighted supermatrix is uT ¼ [0.182, 0.150, 0.106, 0.188, 0.125, 0.185, 0.105], which is the technical feasibility vector for improving each highcarbon element. The same steps could be used to obtain the feasibility vector for improving each element, with cost as the evaluation index, uC ¼ [0.242, 0.138, 0.20, 0.082, 0.103, 0.081, 0.113].

Weighted by a ratio of 50% for technology and cost, Fd ¼ [0.212, 0.144, 0.153, 0.135, 0.114, 0.133, 0.109], i.e., the feasible degree of each high-carbon element in descending order is: usage, material, diameter, toggling waterflow, rotational force, friction and decontamination, and friction effect. The calculation method is detailed in subsection 2.3.4. 3.3. Low-carbon innovative design strategies for the washing machine (1) Structural optimization of the pulsator: The Id and Od of the pulsator diameter are 0.72% and 1.28%, respectively. Larger

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J. Peng et al. / Journal of Cleaner Production 228 (2019) 729e745

100.00% 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00%

Od Fd

Clutch efficiency

Body processing

Usage of pulsator

Puisator's diameter

2.11% 2.06%

1.24% 1.50%

1.53% 2.32%

1.28% 0.70%

Small pieces washing 3.70% 9.70%

Other usage 16.41% 38.32%

Electricity emission factor 4.19% 7.67%

Working time

Recovery rate

Others

Total

3.52% 5.24%

1.16% 3.25%

4.20% 22.98%

43.37% 100%

Core direct elements

100.00% 80.00% 60.00% 40.00% 20.00% 0.00%

Od Fd

Rotating force effect 3.13% 11.52%

Friction drive 7.85% 5.62%

Centrifugal force 0.83% 8.77%

Electromag netic effect 3.27% 32.58%

Mechanical effect 4.43% 14.26%

Physical properties 1.19% 9.30%

Others

Total

3.38% 17.95%

24.08% 100%

Core principles 100.00% 80.00% 60.00% 40.00% 20.00% 0.00%

Od Fd

Washing

Dehydration

3.83% 28.80%

1.27% 9.74%

Deceleration Power function transmission 5.36% 10.32%

9.24% 19.33%

Control function 6.28% 8.25%

Inlet and drain functions 1.20% 3.37%

Balanced and stable

Supporting

Others

Total

0.90% 2.64%

0.70% 4.26%

3.41% 13.29%

32.13% 100%

Core functions

Fig. 8. The essential elements of EB72M2 washing machine.

Table 2 Pair-wise comparison between direct elements of the pulsator. (2) Od: 1. The calculation of Sd is shown in Table 3. When the diameter increases by 1 cm, the carbon footprint increases by 0.061 kgCO2, so, the Sd of the diameter is 0.061 kgCO2/cm. Similarly, by increasing a common unit of the material density, carbon emission factor, washing time, or recovery rate, etc., the Sd of other direct elements could be obtained, after which they are assigned to each indirect element according to the weight value. The calculation method is detailed in subsection 2.3.3.

Material Weight Diameter Processing Transportation Usage Recovery rate Waste treatment

Material

Weight

Diameter

Processing

Transportation

Usage

Recovery rate

Waste treatment

Weight value(u)

1 1/2 1 1/3 1/5 7 1 1/2

2 1 2 1 1/2 3 1 1

1 1/2 1 1/2 1/3 2 1/2 1/2

3 1 2 1 1/2 9 3 2

5 2 3 2 1 9 5 5

1/7 1/3 1/2 1/9 1/9 1 1/7 1/7

1 1 2 1/3 1/5 7 1 1

2 1 2 1/2 1/5 7 1 1

0.118 0.073 0.134 0.048 0.028 0.428 0.092 0.079

Table 3 Carbon footprint sensitivity degree of pulsator elements. Material

Weight Diameter Processing Transportation

Common unit r: 0.1 g/cm3, m: in the 10 g d: design 0.1 kgCO2$kg1

d: 1 cm

E: 0.1 kw$h y: 0.1

distance: 10 km, fuel consumption: 1 L/ 100 km

Sd

0.061

0.065

0.008

0.102

0.23

Usage Recovery Waste rate treatment

Toggling Friction and Rotational Friction waterflow decontamination force effect

x: 0.1 t: 1 min p: 10 w 0.97 0.025

1.242

amount of waste: 10 g

0.03

0.249

1.242

0.249

J. Peng et al. / Journal of Cleaner Production 228 (2019) 729e745 Table 4 The comparison between high-carbon direct elements on toggling waterflow.

Usage Diameter Material

741

Table 6 The comparison matrix of relationships between direct element cluster.

Usage

Diameter

Material

Weight value

1 5 1/2

1/5 1 1/9

2 9 1

0.162 0.783 0.084

C1 C2 C3

diameter results in greater detergency, but a low-carbon design requires the diameter to be as small as possible, thus creating a technical contradiction. With the “length of the moving object” as the parameter requiring improvement, and the corresponding deterioration parameter is “force”. Three principles of invention are obtained from the Altshuller contradictory matrix table: the 17th, onedimensional variable multidimensional, the 10th, preemptive, and the 4th, asymmetric. According to “change the object from one-dimensional to two-dimensional or threedimensional space” in the 17th principle, the pulsator could be made concave, which can ensure the diameter of ribs to guarantee a detersive force while reducing the diameter of pulsator. According to “using multi-layer structure replaces the single-layer structure” in the 17th principle, a circular table-shaped protrusion with a height of about 10 cm can be designed in the middle of the pulsator, which will combine the advantages of the agitator-type washing machine and the pulsator-type washing machine, because an upward vortex can form to prevent clothing winding while increasing detergency. According to the 4th principle, the ribs can be made asymmetrical; and the 10th principle can be associated with washing after soaking, which is not relevant to this design. The conceptual structure of the final design of the pulsator is shown in Fig. 9. The TRIZ method was applied for structural optimization design in section 2.4.

3.4. Program evaluation The preliminary evaluation of aforementioned strategies is

C1

C2

C3

Weight value

1 1/3 1/5

3 1 2

5 1/2 1

0.657 0.147 0.196

Table 7 The weight matrix under technical feasibility

Direct elements

Functions

Principles

Direct elements

0.657

0.263

0.17

Functions

0.147

0.190

0.83

Principles

0.196

0.547

0

shown in Table 9. The carbon footprint of the pulsator was reduced from 28.89 kgCO2 to 26.35 kgCO2, which is not significant. However, its washing force was increased by approximately 30%, which reduces the average washing time by about 7 min. The transmission efficiency was improved by approximately 20%, and the spray washing could save more than 66% on electricity consumption each time. The low-carbon reservation function could reduce the electric emission factor by approximately 1/4th. In addition, the overall size, recovery rate, and user awareness of low-carbon use etc., all can be improved. To sum up, the final carbon footprint can be estimated to have reduced from 533.32 kgCO2 to 314.46 kgCO2; hence, the correctness and practicality of our study is validated. This can guide the innovative design of products with low-carbon footprints in terms of structures, functions, principles, and life cycle process.

Table 5 The primary super-matrix of the pulsator

Usage

Diameter

Material

Toggling waterflow

Friction and decontamination

Rotational force

Friction effect

Usage

0

0.9

0.83

0.162

0.231

0

0

Diameter

0.4

0

0.17

0.783

0.692

0

0.7

Material

0.6

0.1

0

0.084

0.077

0

0.3

Toggling waterflow

0

0.75

0.67

0

0

0.8

0.2

Friction and decontamination

0

0.25

0.33

0

0

0.2

0.8

Rotational force

0.67

0.7

0

0.83

0.5

0

0

Friction effect

0.33

0.3

1

0.17

0.5

0

0

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J. Peng et al. / Journal of Cleaner Production 228 (2019) 729e745

Table 8 Weighted super-matrix of the pulsator

Usage

Diameter

Material

Toggling waterflow

Friction and decontamination

Rotational force

Friction effect

Usage

0

0.591

0.545

0.043

0.061

0

0

0.182

Diameter

0.263

0

0.112

0.206

0.182

0

0.119

0.150

Material

0.394

0.066

0

0.022

0.020

0

0.051

0.106

Toggling waterflow

0

0.110

0.098

0

0

0.664

0.166

0.188

Friction and decontamination

0

0.037

0.049

0

0

0.166

0.664

0.125

Rotational force

0.131

0.137

0

0.454

0.274

0

0

0.185

Friction effect

0.065

0.059

0.196

0.093

0.274

0

0

0.105

Common pulsator d=340 mm, m=748 g Material: ABS (1.45 kgCO2/kg)

Low-carbon innovative designed pulsator d=300 mm, m=720 g Material: Polyethylene(1.19 kgCO2/kg)

Fig. 9. The Low-Carbon Innovative Design for the pulsator. (2) Function optimization of power transmission: The Id and Od of the power transmission function are 19.33% and 9.24%, respectively, whose action composition is shown in Fig. 10. According to SIT, belt rotation is a harmful factor; the belt mechanism should be removed using the strategy “action cutting” (The reorganization strategy for function optimization design in section 2.4), and the drive should be changed to direct drive of the motor to reduce energy loss. In addition, by using the strategy, “action converting” (The expansion strategy for function optimization design in section 2.4), the constant speed rotation of the motor can be changed to a variable speed rotation, which is in line with today’s variable frequency drive technology. Furthermore, because the transmission principle of the belt is that of the friction drive, when the belt needs to be retained, the scientific effect library can use “drive” as the search keyword and principles like friction drive, meshing drive, fluid transmission, and electric drive can be retrieved. Based on meshing drive’s features, high efficiency, stability, etc., the belt drive can be changed to synchronous belt drive, which is both a principle optimization and an action transformation.

4. Discussion and conclusions This study reveals that both direct and indirect elements have an impact on the carbon footprint of a product, and the impact of those elements on the carbon footprint is multiple evaluation dimensions. This section discusses the results of this work. In the introduction, it addressed the study’s main objectives. First, a comprehensive analysis of the elements affecting the carbon footprint of a product throughout its life cycle is needed. Second, in order to identify the key issues in low-carbon design activities, the relationship between various elements and carbon footprint needs to be explored. Third, it is necessary to propose methods and design strategies for innovative product design with low-carbon footprint when devising low-carbon solutions. Traditional use of LCA methods enables the accurate evaluation of the environmental impacts of manufactured products. However, a product’s carbon footprint analysis and low-carbon designs in related studies (Feng et al., 2013; Xu et al., 2015; Zhang et al., 2018)

mostly focus on the design elements directly related to structures in the whole product life cycle. Low-carbon design is an effective way to reduce carbon emissions. Current research mainly identifies challenges of high GHG emissions, key factors contributing to challenges, or high carbon activities, etc. For example (Lu et al., 2018), proposed a method to select the challenges based on the characteristics of carbon emissions for low-carbon design. However, it did not explore low-carbon design comprehensively, and took into account only the key issues generally known and did not include high-carbon functions and high-carbon principles. For lowcarbon design, it is important to evaluate the impact of various design elements on carbon footprint. Since other works (Lu et al., 2017; Scipioni et al., 2012; Su et al., 2012; Toby et al., 2010) introduced methods to account for the impact of the product life cycle stage, broader impact relationships do exist. Therefore, in section 2.3, this work focused on the importance and optimization feasibility assessment of product design information, including direct and indirect elements. For the study of the impact of design

J. Peng et al. / Journal of Cleaner Production 228 (2019) 729e745

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Table 9 The comparison before and after for low-carbon innovative design of washing machine. Assess target

Before

After

Whole machine

P ¼ 415 W, t ¼ 30 min, … GB ¼ 533.32 kgCO2 d ¼ 340 mm, m ¼ 748 g, washing force 100% … GB ¼ 28.89 kgCO2 Single-layer structure The ribs are symmetrical Material: ABS(1.45 kgCO2/kg) Common motor, belt mechanism, reducer etc., Efficiency 100%... Constant speed rotation, belt drive Pulsator stirring Single charge 0.06 Kw.h… None dEF ¼ 0.424 kgCO2/kwh…

P ¼ 300 W, t ¼ 23 min, … GA ¼ 314.46 kgCO2 d ¼ 300 mm, m ¼ 720 g, washing force 130% … GA ¼ 26.35 kgCO2 Multi-layer structure The ribs are asymmetrical Material: ABS(1.19 kgCO2/kg) Variable frequency direct drive, efficiency 120%... Or variable frequency þ timing belt, efficiency 105%... Variable speed rotation, synchronous belt drive Spray washing Single charge 0.02 Kw.h… Grid information service þ appointment, Low-carbon information þ display Low-carbon information þ recycling service, … dEF ¼ 0.3 kgCO2/kwh...

Pulsator

Power transmission

Small pieces washing Low-carbon service system

Friction drive Id=3.18% Od=5.62%

Power transmission Provide power

...

Control system

Motor rotation (Uniform speed) Id=9.02% Od=4.53%

Motor Id=52.8%

Belt rotation... Id=3.18% Od=3.22%

Belt mechanism Id=4.04%

Clutch rotation... Id=3.37% Od=0.83%

Clutch Id=4.81%

Planet wheels rotation... I % Id=2.52% Od=0.39%

Planet wheels Id=3.59%

Pulsator or Inner barrel rotation Id=1.24%,Od=0.27%

Pulsator & Inner barrel

Fig. 10. The core actions of transmission function. (3) Washing principle optimization of small piece washing: The Id and Od of small piece washing in the usages are 9.68% and 3.70%, respectively. The highest efficiency would only be achieved when the washing amount reached 80% of the rated value (Jing-yan, 2012). Since the Id and Od of the principle of rotating force effect are 11.52% and 3.13%, therefore, the innovative design of small pece washing for low-carbon footprint can be carried out from the principle layer specifically. Searched for “removing substances” keyword in the scientific effect library, and obtained mechanical action, adsorption, and chemical reaction and so on, the mechanical action includes vibration, agitation, friction, flushing, etc. Based on the “flushing”, a “spray washing” for small parts can be designed. The water spray, which ejects highpressure water mist, is set in the inner barrel to achieve decontamination while small clothes. such as undergarments, are suspended in the middle. (4) Innovative design of low-carbon service system: The carbon footprint of the washing machine in use stage was calculated at 76.2%, and one of the main factors affecting the carbon footprint was found to be user laundry behavior and habits. As shown in Fig. 11, the low-carbon design of the service system has been carried on, and the strategies are as follows: (1) Supplementing the reservation function of EB72M2JD automatic washing machine with the low-carbon service, which will realize the lowcarbon reservation function. Because the Id and Od of the power discharge factor in the use stage are 7.67% and 4.19%, respectively, and the electricity peak valley dispersion has a huge effect on it by influencing energy efficiency and the proportion of thermal power, the information on electricity consumption of power grid can be provided as service information to the users to realize the function of “grid information service þ appointment”. It is a supplementing product function strategy for the process optimization design mentioned in section 2.4. (2) Although the panel has a function for displaying current status, such as washing time, it does not meet the user’s low-carbon information requirements. Therefore, it is necessary to display information such as power consumption and carbon footprint on the panel. Besides, the entire status can be programmed to work with mobile phone applications to achieve the “low-carbon information þ display and statistics” function. It is a coordinating product function strategy for process optimization design mentioned in section 2.4. (3) Because the Id and Od of recovery rate are 3.25% and 1.16%, respectively, manufacturers could provide low-carbon recycling services; for example, based on the statistics of the carbon footprint information in the usage stage, the relevant recovery clauses can be created by enterprises, which will help improve the recovery rate, to achieve the “low-carbon information þ recycling service” function. In addition, enterprises could also use applications to provide users with low-carbon footprint guidance and other related services, or to set up self-service low-carbon footprint laundry centers.

elements on carbon footprint, two evaluation dimensions, influence degree and optimizable degree, have been advanced by previous research works, which include weight analysis, sensitivity analysis, and optimization ability analysis of the elements affecting carbon footprint. In order to achieve these objectives, a method to obtain an innovative product design with low-carbon footprint based on multi-layer carbon footprint information is proposed. The carbon footprint information model is established from two levels of direct design elements and indirect design elements. By evaluating the influence of various design elements on carbon footprint, different

low-carbon design directions, from the perspective of innovative design, is proposed. The specific research results can be summarized as follows. First, different types of design elements have different impacts on the carbon footprint of products. By combining direct design elements, including product structures, with the indirect design elements, including function, principle, and process, a product design-element information expression modeldmulti-level carbon footprint informationdis established. The design information is divided into direct layer and indirect layer. The direct layer contains structure elements and process elements, while the indirect layer

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J. Peng et al. / Journal of Cleaner Production 228 (2019) 729e745

)DPLO\ :,),

:DVKLQJ PDFKLQH

0RELO SKRQH

Low-carbon information + display & statistics

Grid information service serv r ice + appointment

Power consumption Electricity situation

User-defined baseline

Carbon footprint Low-carbon ranking of users

Washable domain

8VHUV

Low-carbon guidance Low-carbon laundry knowledge, Low-carbon detergent ...

Low-carbon recycling servicee Carbon footprint points

...

Time

Other low-carbon services

Fig. 11. Low-carbon service system.

contains functions and principles. A quantitative method for calculating carbon footprint based on direct elements is proposed. A greenhouse gas product structure tree is constructed through the integration of various information. Second, different levels of design elements have different impacts on a product’s carbon footprint. In this study, two concepts for evaluating the influence degree of each design element on carbon footprint and the optimizable degree are put forth, and the ANP method is used for the calculation. Influence degree refers to the percentage of contribution of each design element to the total carbon footprint of the product throughout its life cycle, which can be used to find high-carbon design elements in the hierarchy tree. The optimizable degree is the technical feasibility and cost of lowcarbon design for design elements, and it can be used to identify the core design elements in the hierarchy tree. Third, based on the core elements, low-carbon innovative design strategies from four aspectsdstructure optimization design, function optimization design, principle optimization design, and process optimization designdare proposed. The design methods, standards and principles corresponding to the characteristics of each design type are given to support designers in obtaining comprehensive and effective design schemes in the design process. One of the key contributions of this work is a method for finding core design issues in the multi-layer elements of structure, function, principle, and process. These elements affect the product’s carbon footprint and are likely to pose key issues in design, which means that broader issues need to be considered in the low-carbon design process. Therefore, this study proposes an innovative product design method that is based on multi-layer carbon footprint analysis. However, further studies are needed in the future for (1) exploring more ways to study the relationship between the multi-layer design elements and carbon footprint, or more fully studying the relationships, such as influence degree and optimizable degree, proposed in the study. (2) Researching further the strategies of innovative product design method for low-carbon footprints and forming a methodology for low-carbon design strategies that can support the development of relevant auxiliary

tools on computers. Acknowledgements This work is supported by theNational Natural Science Foundation of China (Grant No. 51435011) and the Science & Technology Ministry Innovation Method Program China (Grant No. 2017IM040100) and Sichuan university & Luzhou strategic cooperation project (2017CDLZ-G11). Appendix

Abbreviations List acronym full form GHG Greenhouse Gases LCA Life Cycle Assessment BOM Bill of Material TRIZ/TIPS Theory of Inventive Problem Solving QFD Quality Function Deployment G-PST Greenhouse Gas-Product Structure Tree Id Influence degree Od Optimizable degree ANP Analytic Network Process FBS Function-Behavior-Structure DE Direct Elements IE Indirect Elements SE Structure Elements PE Process Elements F Functions P Principles A Actions PST Product Structure Tree DI Design Information C-S-D Carbon footprint-Structure-Design information S1 Acquisition Stage S2 Manufacturing and Assembly Stage

J. Peng et al. / Journal of Cleaner Production 228 (2019) 729e745

S3 S4 S5 IdD IdI U-e L-e Imd Fd Sd SIT

Transportation Stage Use Stage Recycling Stage Influence Degree of Direct Element Influence Degree of Indirect Element Upper-Layer element Lower-Layer elements Importance Degree Feasible Degree Sensitivity Degree Systematic Inventive Thinking

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