Sustainable transportation: an overview, framework and further research directions
Rameshwar Dubey* Symbiosis Institute of Operations Management, Symbiosis International University, Plot No. A-23, Shravan Sector, CIDCO, New Nashik – 422008, India Email: [email protected] *Corresponding author
Angappa Gunasekaran Charlton College of Business, University of Massachusetts Dartmouth, North Dartmouth, MA 02747-2300, USA Fax: (508)-999-8646 Email: [email protected] *Corresponding author
Abstract: In our study we have attempted to develop a theoretical framework for sustainable transportation based on synthesis of exhaustive review of extant literature. To test the framework statistically, a structured questionnaire was designed. Measures for the questionnaire were adopted from extant literature and to operationalise the constructs further, the questionnaire was to be pretested. Adding to this, the validity of the constructs was examined using confirmatory factor analysis followed by partial least square – structural equation modelling to test research hypotheses. The statistical analyses suggest that model exceed the threshold limit for goodness of fit test. Except government policy, all five research hypotheses are accepted. The present study is a unique contribution in terms of its theoretical implications and practical use. Finally, our research findings were concluded and further research directions were outlined.
Keywords: sustainable transportation; clean energy; construct validity; partial least square; PLS; structural equation modelling; SEM.
Reference to this paper should be made as follows: Dubey, R. and Gunasekaran, A. (xxxx) ‘Sustainable transportation: an overview, framework and further research directions’, Int. J. Shipping and Transport Logistics, Vol. X, No. Y, pp.xxx–xxx.
Biographical notes: Rameshwar Dubey is currently associated as an Associate Professor at Symbiosis Institute of Operations Management, Nashik. Beside teaching, he is an active Researcher, who consistently contributes his research output to reputable journals in the field of operations and supply chain management. He is also a regular reviewer of reputable journals in the field of
2 R. Dubey and A. Gunasekaran
operations and supply chain management. He is currently associated as an Associate Editor of Global Journal of Flexible Systems Management and editorial board members of IJIS and AIMS International Journal. His area of research interest lies in sustainable supply chain management, humanitarian supply chain management, flexible manufacturing systems, manufacturing strategy, qualitative research methods, and multivariate statistics.
Angappa Gunasekaran is a Professor of Operations Management in the Department of Decision and Information Sciences at the Charlton College of Business, University of Massachusetts, Dartmouth (USA). He has over 250 articles published in 40 different peer-reviewed journals. He has presented over 60 papers and published about 60 articles in conferences. He is on the editorial board of 25 journals. He edits journals in operations management and information systems areas. He is currently interested in researching benchmarking, information technology/systems evaluation, technology management, logistics, and supply chain management.
1 Introduction
Transportation is one of the fields that have attracted serious attentions from economists in past. However, in recent years, goods transportation has attracted burgeoning interests among supply chain management community including logistics and operations management field. Nevertheless, clean transportation is a growing concern, which includes both public and goods transportation (Eyring et al., 2010). Transportation contributes nearly 5.5% of over 50,000 mega-tonnes of CO2 on an average emitted during a year (World Economic Forum/Accenture, 2009). It is widely recognised by the policy makers, practitioners and academicians that logistics and transport operations have a negative impact on the ecological balance (Murphy and Poist, 2003). Transportation has always been linked with the advancement of society. It is regarded as one of the forces of globalisation. However, with the emergence of modern transportation, the speed has increased but at the cost of our own planet. Heavy reliance of modern transportation on natural fuels has posed major. The continuous release of greenhouse gases (GHG) such as CO2 and CH4 from the development of various energy-intensive industrial activities has ultimately caused human civilisation to pay its debt (Shafiee and Topal, 2009). In developing economies, particularly in India and China, there is an absence of reliable access to clean energy. Consequently, the low-income groups who cannot afford expensive resources are exposed to severe threat of diseases and poor health, which further impedes the development process (Haines et al., 2007). Furthermore, current patterns of fossil-fuel use cause substantial ill health from air pollution and occupational hazards (Mohammadi et al., 2014). Impending climate change, mainly driven by energy use, now threatens health too. The answer to these challenges is to address them
Sustainable transportation 3
proactively through sustainable SCM and logistics practices, which include incorporating inventory and service level requirements, modelling potential manufacturing locations and warehouses, optimisation of transport processes and CO2 reduction, and closing of materials flows by improvement of post-sale logistics operations among others.
All this puts lot of pressure on the national governments to devise policies for reducing greenhouse gas emissions as well as oil demands. Transportation, being a major contributor to greenhouse gas emissions, is the prime target for reducing air pollution and obtaining sustainable environment. This leads to Green Transportation, which means any kind of transportation practice or vehicle that is eco-friendly and does not have any negative impact on the immediate environment.
Green transportation involves effective and efficient resource utilisation, changes in transportation structure and making healthier travel choices. This demands enhanced public awareness and participation, control of private vehicles and development of vehicles powered by renewable energy sources like solar, wind, electricity, biofuels, etc. Transportation is regarded as the fastest growing sector, in terms of energy consumption (Gray and Frost, 1998).
While there is a rich body of literature focusing on enablers of sustainable transportation (e.g., Richardson, 2005; Lai et al., 2013; Guerrero et al., 2013; Acciaro, 2014); most of the research either focuses on shipping companies or road transportation companies. Second, there is hardly any empirical study in context of Indian transportation companies that include both shipping and trucking companies. Objectives of our study are:
1 to identify the critical success factors of sustainable transportation
2 to develop a conceptual framework for sustainable transportation
3 to empirically validate our conceptual framework
4 to outline further research directions.
The present paper is organised as follows. The next section presents the critical review that has been undertaken in a systematic approach. In the third section, theoretical framework and research hypotheses have been discussed. In our fourth section, the research design has been outlined, where the instrument design and its measures, the pretesting of instrument, sampling design, data collection procedure and response biasness test have been discussed. In the fifth section, the psychometric testing of instrument and hypothesis testing is detailed. The sixth section concludes the research findings and outlines our unique contribution in the present study. Further, the limitations have been identified and research directions are derived.
4 R. Dubey and A. Gunasekaran
2 Review of related research, theoretical framework and research hypotheses
In this section, an attempt has been made to outline the various stages of the scientific process. The stages involved in our study are outlined as:
• Problem identification
In our study, we have identified our research problems through two important sources:
First, the manager’s opinion on sustainable transportation and their present outlook related to sustainable transportation framework and its limitations.
• Literature review
The literature review includes scientific papers published in reputable journals, which are indexed in Scopus or SCI/SSCI or both, reports published by reputed agencies, edited books and trade magazines. Our attempt was to include latest papers or reports; however any reports or articles relevant to our present study were also included to justify our overall objectives of the present research. Further, the outcomes of expert interviews and extant literature were synthesised to define our research objectives that have been outlined in our introduction section.
2.1 Sustainable transportation
Surface transport plays a key role in people’s everyday lives and is a decisive factor in economic competitiveness and employment. From an environmental and resource point of view, in front of surface transportation (road, rail, water, etc.), use of clean energy is the major issue (Gray and Frost, 1998). There are various reasons of concern as the carbon emissions need to be reduced at the same time and energy needs to be conserved.
The world energy market is still dominated by fossil fuels, where incremental changes can have a large influence on efforts to attain sustainability (Goldemberg, 2006). Renewable energy sources are key to achieve this goal (Shafiee and Topal, 2009). Excluding traditional biomass, renewables represented insignificant value of primary energy consumption, unevenly distributed between developed and developing countries. Environmental problems at local, regional and global levels, as well as external dependency and security of supply will persist if we rely on an energy future based on fossil fuels (Goldemberg, 2006). Chalk and Miller (2006) outlined in their research that the use of battery and fuel cells can provide sustainable solution to existing transportation sector. It will further help to reduce or eliminate dependency of present transportation system on transportation consuming diesel or petroleum. Windecker and Ruder (2013) conducted a study in which they have ranked alternative fuels (AFs) on the basis of cost, environmental performance and efficacy. In one such study (Sorrentino et al., 2014), the
Sustainable transportation 5
need for electric operated vehicles was expressed and impacts of complete electrification on GHG emissions were examined. However, there are contributions in the direction of use of clean energy in transportation particularly in context to road transportation. Guerrero et al. (2013) developed a model known as truck sector optimisation (TSO) model, which helps the shippers or carriers to assess the cost-benefit analysis for the investment in environmental technology, which helps to reduce GHGs. Acciaro (2014) recommended the use of LNG engines for shipping sector in future to reduce the sulphur emissions in air and further examined the economic viability of the model. Yang (2012) in one of the articles, conducted an empirical research on Taiwanese maritime firms. The study concluded that environmental management initiatives of Taiwanese maritime firms have significant positive impact on environmental performance. In one of the interesting studies (Lai et al., 2013), the researchers have attempted to develop a measurement scale for green shipping practices. The constructs identified that leads to green shipping practices (GSP) are company policy and procedure (CPP), shipping documentation (SD), shipping equipment (SE), shipper cooperation (SC), shipping materials (SM) and shipping design for compliance (SDC). In this section, an attempt has been made to provide an overview of the need for sustainable transportation. In the discussion, some extant literature has been reviewed, which outlines the need for battery or fuel cell, electric vehicles, hybrid technology and AFs. However, in spite of so many options, the road transportation continues to rely more on diesel consumption. In the next section, the extant literature will be critically reviewed to identify the building blocks of our theoretical framework.
2.2 Building blocks of sustainable transportation
The foundation of our conceptual framework comprises macro-factors that influence the assimilation of sustainability in transportation (see Figure 1). There is numerous political, economic, social, technology, legal and environment (PESTLE) studies over the years (McKinnon and Kreie, 2010). However, the interaction among these macro factors were not explored in past from Indian transportation perspective and most of studies focussed on single dimension. To answer the pressing call of the time, we have proposed an integrated conceptual framework. In our present study, government policy, economic situation, social dimensions, technology, legal structure and environmental dimensions (GESTLE) framework has been adopted, which is similar to PESTLE framework. This framework is the result of synthesis of exhaustive literature. In this section, the extant literature is classified based on the major building blocks of the proposed conceptual framework for sustainable transportation (see Table 1).
6 R. Dubey and A. Gunasekaran
Table 1 Classification of literature on sustainable transportation
Classification criteria References
Government policy Konur and Schaefer (2014), Lai et al. (2013), Yi et al. (2013), Frynas (2012), Yang and Hu (2013), Qi and Song (2012), Nealer et al. (2011), Richardson (2005) and Hooper (1997)
Economic situation Konur and Schaefer (2014), Guerrero et al. (2013), Golicic et al. (2010), Amekudzi et al. (2009), Steg and Gifford (2005) and OECD (1976, 1982)
Social dimensions UNDP (2011), Amekudzi et al. (2009), Vlek and Steg (2007), Litman (2007), Litman and Burwell (2006), Richardson (2005) and Steg and Gifford (2005)
Green technology Nguene et al. (2011), Golicic et al. (2010), Amekudzi et al. (2009), Steg and Gifford (2005) and Greene and Wegener (1997)
Legal structure Yang (2011), Maes (2008), Toleman and Rose (2008), May and Crass (2007), Vieira et al. (2007), Bichou et al. (2007) and Šakalys and Palšaitis (2006)
Environmental dimensions Lai et al. (2013), Yang (2012), Litman and Burwell (2006) and Richardson (2005)
In this, the enablers of sustainable transportation have been broadly classified into six building blocks which will be discussed in detail in the present section.
Figure 1 Sustainable transportation framework
2.2.1 Government policy
Government policy is critical in achieving sustainable transportation. The guidelines are formulated by various ministries, which include ministry of shipping, ministry of road transport and highways and ministry of railways with the aim to prevent accidents, improve quality of life and promote sustainable development. The term ‘government policy’ can be used to describe any course or principle of action adopted or proposed by a government that intends to change the situation (Source: New Oxford Dictionary). The
Sustainable transportation 7
government may use policy or policies to tackle a wide range of issues related to transportation like poor road condition or highways, to put a check on carbon emissions or oil spills in water bodies through legislations like OPA (1990), The Clean Water Act (1972), the sustainable dredging, Environment Protection (Sea Dumping) Act (1981), the Environmental Protection and Biodiversity Conservation Act (1999) and the Motor Vehicles Act (1988) and its amendment Act 27 of 2000, amendment Act 39 of 2001, etc. Through these legislations, the government binds the private owners of the shipping companies or road transporters, who generally tend to violate these regulations for short term benefits within a regulatory framework (e.g., Lai et al., 2013; Yi et al., 2013; Konur and Schaefer, 2014). Thus, we can hypothesise as:
H1 Government policies have positive impact on sustainable transportation.
2.2.2 Economic situation
The economic criteria are determinants of an economy’s performance that directly impacts sustainability of transportation and have resonating long term effects. For example, the hike in diesel or petrol price may have adverse impacts on sustainability of transportation. It leads to overloading of trucks and wagons, may pose major threat to road, the truck life cycle, and carbon emissions. The shipping companies may tend to shy away from their responsibilities towards cleaning of water bodies, which are polluted significantly due to the increased trade movements (e.g., Golicic et al., 2010; Guerrero et al., 2013). Hike in the fuel prices has significantly impacted the speed of the fleet, thus impacting the on-time delivery (Maritime Administration, US Department of Transportation, 2008). Therefore, it can be hypothesised as:
H2 Economic situation have positive impact on sustainable transportation.
2.2.3 Social dimension
These factors scrutinise the social environment that spell the need for sustainable transportation, and gauge determinants like cultural trends, demographics, population analytics, etc. (Midilli et al., 2006). An example could be growing trends among rural India towards use of two-wheelers and four-wheelers. The increased awareness and buying ability among rural people has fuelled the growth of automobiles industry in India and other Asian countries but at the cost of climate change. The increasing pollution level and rapid change in use of land pattern in the form of highways, bridges and railway track has seriously impacted the ecological balance. The irresponsible behaviour of people towards river has forced the sacred river like Ganges to the verge of extinct. The unemployment rate, poor education and increasing disparity among various classes in India or Asian countries have seriously impacted the transportation mix in recent years is the subject of further investigation (e.g., Vlek and Steg, 2007; Litman, 2007; UNDP, 2011). Based on preceding discussions it can be hypothesised as:
H3 Social dimensions have positive impact on sustainable transportation.
2.2.4 Green technology
The use of green technology has seen an exponential rise. In recent years, the oil spill detection technology, pollution control technology and use of batteries have major roles
8 R. Dubey and A. Gunasekaran
to play in the development of clean, fuel-efficient vehicles. In fact in recent years, the success of hybrid technology cars can be clearly attributed to the use of batteries. The fuel cells are estimated to have energy efficiency twice that of internal combustion engines (Chalk and Miller, 2006). However, before these technologies can be commercially used, there are issues that need to be resolved. The first issue is related to durability and second issue is related to the total cost of ownership. A recent development in electro catalyst has shown that Pt alloys have shown better activity than Pt catalysts. Moreover, the government has a major role to play in creating awareness among the transporters and shipping companies to use green technology (e.g., Golicic et al., 2010; Nguene et al., 2011). We therefore can hypothesise as:
H4 Green technologies have positive impact on sustainable transportation.
2.2.5 Legal structure
In recent years, the role of legal structure and their impacts on sustainable transportation have attracted serious attention from researchers and practitioners. Sarkis et al. (2011) in one of their articles clearly outlined the contribution of researchers from organisational theory perspective. The institutional theory provides a theoretical lens to examine the need for sustainable practices, other than economic reason (Glover et al., 2014). In past, scholars have clearly outlined the need for government role in linking sustainable practices with legitimacy. Also, there is plenty of literature where scholars have outlined the sustainable practices as a CSR activity (e.g., Bai and Sarkis, 2010; Wong et al., 2012). In our present study, our discussion is restricted to the domain of legitimacy, which is critical for strong governance. This has led us to hypothesise as:
H5 Legal structure has positive impact on sustainable transportation.
2.2.6 Environmental dimensions
The environmental dimensions are critical for achieving sustainability. There is a growing concern towards environment protection and it is quite heartening to see that in recent years, the rate at which researchers have addressed the grave concern has been well appreciated across the globe. The sustainability reports of the companies and government reports are clear indicators of the growing trends among society and the awareness level towards sustainable practices. However, in spite of several efforts made by NGOs and government agencies, there are serious violations of environmental norms. The rapid changing land patterns and the rate of deforestation are having serious impacts on ecological footprints. In past, the major efforts where towards controlling carbon footprints do not reflect the holistic efforts towards achieving ecological balance of our planet. The environmental dimensions related to clean atmosphere are protection of trees, preservation of forests and rivers, cleaning of water bodies and regular monitoring of effluents being continuously discharged by industrial bodies into rivers and sea. Transportation that includes air, road, rail and water is main source of pollutions that are next to industrial pollutions in developing countries and in developed economies, transportation is the major source of pollution. The increased trade activities in recent years and frequent accidents in sea, which have caused potential damage to marine lives, are the important issues which will be addressed in the present research. Therefore, it can be hypothesised as:
Sustainable transportation 9
H6 Environmental dimensions have positive impact on sustainable transportation.
An in-depth literature review was followed by interviews with experts who have either published their works in reputable journals related to sustainable transportation or senior managers having huge exposure to sustainable practices in their shipping companies or in transportation firms. There is clear consensus among our findings and the expert opinions regarding lack of comprehensive sustainable transportation framework, which provides clear guidelines in terms of accountability. Second, in past there are efforts attempted to develop a framework and performance systems for sustainable transportation, however, there is dearth of comprehensive framework focusing on sustainable surface transportation.
The research process will further discuss how to test the research hypotheses that includes questionnaire design, sampling design, data collection and non-response biasness test.
3 Research design
In this section, an attempt has been made to develop an instrument to measure the constructs and test research hypotheses. The instrument has been developed scientifically. Once an instrument has been developed, it was pretested to further operationalise each constructs and their items that were included in the instrument. The samples were identified from the database of Container Shipping Lines Association (India), which is popularly known as CSLA, presently having 34 members, Central Institute of Road Transport (CIRT) and All India Transporters Welfare Association (AITWA). Once the samples were identified, the data were collected using Dillman’s (2007) total design method followed by non-response bias test to ensure that data collected were free from non-response biasness.
3.1 Measures
Table 3 summarises the scales for the framework in Figure 1. The measures were adopted or modified from scales established in extant research to avoid scale proliferation. We used multi-item measures for constructs for our theoretical framework to improve reliability, reduce measurement error, ensure greater variability among survey individuals, and improve validity (Churchill, 1979). The instrument was pretested before a questionnaire was e-mailed to respondents.
Each building block consisting of items was assessed using a five-point Likert scale, ranging from ‘1 = strongly disagree’ to ‘5 = strongly agree’. As regards, the sustainable transportation performance that consisted items representing environmental performance, social performance and economic criteria was assessed using a five-point Likert scale, ranging from ‘1 = very poor’ to ‘5 = very good’ was used.
10 R. Dubey and A. Gunasekaran
3.2 Data collection
Data was collected through an electronic survey, which was split into three parts. The first part consisted of factors related to six specific factors that are building blocks of a theoretical framework and the second part consisted of factors related to firm-level analyses (e.g., the firm performance).
The complete survey was sent to targeted individuals who are heading the operations in their respective firms. These individuals were requested to pass on the third part to their colleague in marketing and finance department, who is more knowledgeable with respect to firm-level questions. The split survey method allowed us to address the most informed respondents to answer our questions. The initial sample frame consisted of 261 firms and was compiled from CSLA, CIRT, AITWA and SCI. The databases were chosen to reach to maximum number of executives of sufficient seniority and knowledge to answer the split survey. Moreover, the two industries of main interest were: shipping and road transportation, since these were considered to be crucial to transportation sector in terms of volume handled and the complexity involved. The data was collected and followed by Dillman’s (2007) total design method. The survey was sent to the potential respondents in Microsoft document version in an e-mail attachment, and followed up with phone calls. Depending on preference of the potential respondent, surveys were answered via e-mail, fax or mail. Overall, 100 complete and usable responses were received. The returned response represents 46.30 (approx.) of the total targeted respondents.
The representation of the characteristics of the respondents in Table 2 is as under: Table 2 Respondents profile
Number of respondents
Percentage of respondents
President 3 3 Vice-president 25 25 General managers 45 45
Note: aRepresents one employee did not provide this information.
From Table 2, we can draw conclusion that road transportation sector represents 20% of the total respondents and 55% represents liner shipping companies and liner shipping agencies. However, in our study we have received 25% response from Freight forwarders, which play an integral role in supply chain network. In our study, 95% of the respondents were having over ten years of experience in transportation business (i.e., in road transportation or shipping industry or in freight forwarding business). The 28% of respondents are very senior level person, involved in a strategic decision making and 45% are either in charge of a strategic business unit or heading a division, 27% represents managers who are involved in implementation of policies, 11% of our respondents represent firms having more than 500 employees, 18% of the respondents represent firms having employees between 250 and 500, 37% of the respondents represent firms having employees between 100 and 249 and 34% of the firms having less than 100 employees.
3.3 Non-response bias
Non-response bias is the difference between the answers of respondents and non-respondents (Lambert and Harrington, 1990). To provide support for non-responses bias we compared the responses of the early and late waves of the returned surveys (Yang, 2012; Lai et al., 2013; Armstrong and Overton, 1977; Lambert and Harrington, 1990). The final samples of study were split into equally-sized groups. The first group is referred as early wave and second group is referred as late wave. Paired sample t-tests performed on the two groups yielded no statistically significant differences (p > 0.05). The results suggest that the non-response bias is not a serious concern in our dataset.
12 R. Dubey and A. Gunasekaran
4 Data analysis
A two-step approach suggested by Anderson and Gerbing (1998) was employed to analyse the data. In the first step, the confirmatory factor analysis was performed to assess the psychometric properties of the constructs (i.e., construct validity). The second step then requires the structural equation model from the latent variables. All analyses were carried using SPSS 20.0 for Windows and Smart PLS, version 2.0 M3, an open-source software package, which is provided by the University of Hamburg (Meena and Sarmah, 2012; Ringle et al., 2005).
4.1 Assessment of psychometric properties
Before evaluating the reliability and validity of the measurement items, the indicators for the assumption of constant variance, existence of outliers, and normality plots have first been tested. The residual plots, rankits plot of residual and statistics of a skewness and kurtosis were used. To detect multivariate outliers, the Mahalanobis distance of predicted variables was used. The maximum value of skewness and kurtosis of the indicators in the remaining dataset were found to be 1.92 and 5.43, respectively. These values were well within the limits recommended by past research (skewness < 2, kurtosis < 7) (Curran et al., 1996). Finally neither the plots, nor the statistics indicated any significant deviances from the assumption.
To ensure that multicollinearity was not a problem, the variance inflation factors (VIF) were v calculated. All VIFs were less than 2 and therefore, considerably lower than recommended threshold value of 10.0, suggesting that multicollinearity is not a major issue (Hair et al., 1998). Confirmatory factor analysis (CFA) was used to establish convergent validity and unidimensionality of factors as shown in Tables 3 and 4 respectively. Table 3 Overview of measurement items (factor loadings, scale composite reliability and
average variance extracted)
Scale Items λi Variance Error AVE
Does government regulations provide clear guidelines in
controlling pollution level
0.739 0.546121 0.453879
The infrastructures such as (road, bridge or port) are enough to support
transportation
0.794 0.630436 0.369564
Government provides necessary supports for
smooth security clearance
0.75 0.5625 0.4375
Government policies (X1) SCR = 0.843 Cronbach’s alpha = 0.712
The government has relaxed ECB funding norms for shipping
industry
0.742 0.550564 0.449436
0.57240525
Note: λi represents factor loadings of each item.
Sustainable transportation 13
Table 3 Overview of measurement items (factor loadings, scale composite reliability and average variance extracted) (continued)
Scale Items λi Variance Error AVE
Diesel price 0.741 0.549081 0.450919 The price of lubricants,
lube, tyres and other ancillaries
0.761 0.579121 0.420879
The inflation rate 0.725 0.525625 0.474375 The tax on Indian crew 0.725 0.525625 0.474375 The operating cost for Indian cargo is high in
From Table 3, the government policies (X1) can be expressed as a linear combination of items which are loaded on X1 as:
1 0.739*( ) 0.794*( )0.75*( c ) 0.742
*( ) 1
X government regulation infrastructurese urity clearance
ECB funding norms for shipping industry e
= ++ +
+
The above linear expression suggests that a government policy, in this paper refers to linear combination of government regulation, infrastructure, security clearance and ECB funding norms. The factor loadings of infrastructure is 0.794 is followed by security clearance (0.75), ECB funding norms of shipping industry (0.742) and government regulation (0.739). However, the magnitude of the error for each loaded items is well below 0.5 which is within accepted range.
Economic condition, which is an important construct of our framework, can be expressed as a linear combination of items that are loaded on it as:
2 0.741*( ) 0.761*( , , ) 0.725*( ) 0.725*( ) 2
X diesel pricelubricants lube tyre and other ancillariesinflation ration tax on crew e
= ++
+ +
Sustainable transportation 15
The expression indicates that economic condition for achieving sustainable transportation is determined by lubricants, lube, tyre and other ancillaries’ price (0.761). This is further followed by diesel price (0.741), inflation rate (0.725) and tax on crew (0.725). The price of the diesel and tax on crew members are the important barriers that impact the sustainable transportation mission. The diesel price in India is even higher than other countries in Indian subcontinent. Second, the shipping industry at present is suffering from acute shortage of skilled seafarers.
The social dimension is an important macro factor, which has an important impact on assimilation of sustainability in transportation. It can be further expressed as a linear combination of items loaded on it (see Table 3) as:
X unemployment rate literacy rateIndian culture poverty levelcorruption practices sanitation level e
= ++ ++ + +
Social dimension has an important influence on sustainable transportation as reported in past literature. Our study has revealed an interesting composition of social dimension, which has significant impact on sustainable transportation. The literacy rate (0.957) determines the awareness level among people who are an important part of the society. It is followed by Indian culture (0.818), unemployment rate (0.757), sanitation rate (0.731), poverty level (0.712) and corruption level (0.711). The impact of nation culture, explained by Hofstede (1986), clearly determines the degree of assimilation of sustainable practices in transportation sector. Further, the study reflects the impact of sanitation, poverty level, and corruption on sustainable practices in transportation. Our study has an interesting revelation which needs further empirical investigation. It has been discussed in the conclusion section under further research directions sub-section.
Green technology adoption has an important impact on sustainable transportation (see Table 1). Our theoretical framework (see Figure 1) hypothesises that green technology, has a significant impact on sustainable transportation. Our study identified items that are loaded on green technology factor and expressed as a linear combination as:
4 0.864*( ) 0.792*( ) 0.773*( ) 4
X green design pollution control devicessensory devices e
= ++ +
The green design (0.864) helps in wastage reduction and is found to be very significant component of green technology for sustainable transportation. The green design is followed by pollution control devices (0.792) and use of sensory devices (0.773), which are significant items of green technology adoption for sustainable transportation.
The legal structure of any country or state plays an important role. Based on the extant literature review, the legal structure has been hypothesised as important exogenous variable of our theoretical framework has shown in Figure 1. There are five items loaded on legal structure that can be expressed as a linear combination as:
5 0.818*( ) 0.731*( )0.731*( ) 0.721
*( ) 0.711*( ) 5
X Motor vehicle act The merchant shipping actThe multimodal transportation of goods act
International maritime organisations conventionClean water act e
= ++ +
++
16 R. Dubey and A. Gunasekaran
The legal structure is one of the significant factors that influence the sustainability of transportation. The motor vehicle act (0.818), the merchant shipping act (0.731), the multimodal transportation of goods act (0.731), international maritime organisations convention (0.721) and the clean water act (0.711) are important components of legal structure, which influence the successful assimilation of sustainability practices in transportation.
The environmental dimension is hypothesised in our study as an important variable that impacts sustainable practices. The exploratory factor analysis output suggests that four items are loaded on environmental dimension. The environmental dimension can be expressed as a linear combination of four items as:
6 0.828*( ) 0.79*( ) 0.774*( ) 0.744*( ) 6
X reduction in air pollutiontreat effluents being discharged into water bodiesclean water bodies reduce water pollution e
= ++
+ +
Reduction in air pollution (0.828), followed by treatment of effluents before discharging into water bodies (0.79), cleaning water bodies (0.744) and reduction of water pollution (0.744) are the important components of environmental dimensions.
The endogenous variable of our framework is ‘sustainable transportation’ as shown in Figure 1, which can be expressed as a linear combination of eight items that are found to be loaded on sustainable transportation. The sustainable transportation can be expressed as a linear combination as:
Y customer satisfaction paid leaveregular medical check up to your employeeseco friendly services market sharebetter facilities to seafarer o
= + ++
+ ++ s ) 0.708
*( ) 7perating co t
firm dispose end of life products after use as per environmental norms e+
+
Sustainable transportation involves components like customer satisfaction (0.833), paid leave (0.766), regular medical check-up of the staff (0.734), eco-friendly services (0.712), market share of the organisation (0.712), better facilities to seafarer (0.711), operating cost (0.711) and disposal norm (0.708).
In Table 3, we can see that the standard loadings in each case is greater than 0.5, the composite reliability is found to be greater than 0.7 and average variance extracted (AVE) value is greater than 0.5. These values are greater than the proposed threshold value for possessing convergent validity (see, e.g., Lai et al. 2013; Yang, 2012; Fornell and Larcker, 1981). In Table 4, the discriminant validity of constructs of our framework shown in Figure 1 has been checked.
From Table 4, we can see that the square root of the construct’s AVE is greater than the off-diagonal numbers in the same row and column. Hence, we can conclude that constructs of our framework in Figure 1, possess discriminant validity. Thus, we can draw conclusion that our constructs of framework possess construct validity.
Notes: *The square root of the construct’s AVE is provided along diagonal. **Off-diagonal numbers are the Pearson correlation coefficients between the
constructs.
4.2 Hypotheses test using PLS
Literature supports two methods to estimate structural model, namely, one is LISREL or AMOS-based structural equation modelling (SEM) also called covariance-based SEM, and the other one is partial least square (PLS), which is a variance-based approach developed by Wold (1975). Here PLS has been used as opposite to AMOS or LISREL or any other covariance-based methods, as it requires smaller sample size to test the conceptual model. Second, this approach does not require data to be normally distributed (Fornell and Cha, 1994; Wold, 1975). However, in our case data is normally distributed but sample size is 100.
The PLS produced coefficients of multiple determination (R2) for all endogenous constructs in the model. In PLS analysis, the criteria for evaluating the structural model is the coefficient of determination R2 and significance level (Chin, 1998). Falk and Miller (1992) proposed that the value of R2 should be greater than 0.1. The results of the structural model are presented in Table 5. Table 5 Hypotheses testing results
Path R2 Path coefficient
Standard error P-value Decision
X1 → Y 0.544 –0.440 0.041 0.000 Not supported X2 → Y 0.476 0.591 0.063 0.000 Supported X3 → Y 0.659 0.598 0.043 0.000 Supported X4 → Y 0.689 0.533 0.036 0.000 Supported X5 → Y 0.164 0.349 0.079 0.000 Supported X6 → Y 0.233 0.467 0.086 0.000 Supported
From Table 5, we can see that except path ‘X1 → Y’, all the paths are found to be statistically supported. The coefficient of determination (R2) is found to be greater than recommended value 0.1. Path ‘X1 → Y’ is found to have negative impacts on sustainable transportation development. In case of the responses of the shipping and road
18 R. Dubey and A. Gunasekaran
transporters, the government has failed to take initiatives to provide enough infrastructures to boost growth in volumes of the business. The length of the road and condition needs serious attention. The port facilities in India are not comparable in terms of facilities like cargo handling or warehousing support or any basic facilities that countries like China, Malaysia, Thailand and others within the same continent offer. Based on response, we can see that green technology adoption, social dimensions and economic conditions are important determinants for sustainable transportation development. However, the legal and environmental dimensions are also found to be significant determinants but the beta coefficients are comparatively less. This indicates that our legal structure needs lot of amendments and the role of government is pivotal to success.
4.3 Goodness of fit of the model
According to Tenenhaus et al. (2005), the goodness of fit (GOF) has only one measure in PLSSEM. Therefore, the average of R-square and the geometric mean of AVE for the endogenous construct are shown in the following formula:
( )2GOF *R AVE′ ′= √
Here 2R′ average value of R-square
AVE ′ geometric mean of AVE.
GOF sqrt (0.442*0.677)=
Here 2R′ 0.442 and geometric mean of AVEs = 0.677.
We obtained the value for GOF = 0.548, which is considered as large, in turn referring to the adequacy of the model validity as proposed by Wetzels et al. (2009).
5 Discussion
In this paper, a theoretical framework for sustainable transportation development for road and shipping transportation has been proposed. The theoretical framework was an attempt to integrate the macro issues for Indian road transporter and shipping companies based on review of extant literature and expert opinions. An instrument has been developed in a scientific way using extant literature as discussed in Section 3.1 and further pretested using experts, identified from academics and senior level practitioners with over 25 years of experience in the field of policy making and implementation. The questionnaire was finalised, followed by data collection from respondents after identifying reliable databases to get accurate responses. Data was collected from 100 usable respondents. To further ensure our data free from non-bias, a non-response bias test was performed as recommended by Armstrong and Overton (1977). The t-tests performed on two samples, i.e., early wave and late wave yielded no statistically significant difference.
Sustainable transportation 19
The data were further analysed using confirmatory factor analysis to check the validity of constructs of our framework. The confirmatory factor analysis revealed that our constructs met the convergent validity and discriminant validity criteria as recommended in past research (i.e., Fornell and Larcker, 1981). The Cronbach’s alpha for each constructs of our framework is found to be greater than 0.7, which suggests that uni-dimensionality of all constructs is acceptable (Tenenhaus et al., 2005). The SEM analysis using PLS suggested that the model exceeded the GOF threshold value as recommended (see for an, e.g., Wetzels et al., 2009). Therefore, it can be concluded that the theoretical framework proposed by us for sustainable transportation development framework is found to be a good one for sustainable transportation in road and shipping sector. Our present study is an attempt to further support similar studies conducted in Asian countries (e.g., Doudnikoff et al., 2014; Acciaro, 2014; Lai et al., 2013; Lu et al., 2013; Yang, 2012). The findings of our present study also support past research (e.g., Richardson, 2005). Through this paper, an attempt has been made to answer the pending calls of the past researchers (e.g., Litman and Burwell, 2006). The contributions of the present study are further discussed in following sub-sections.
5.1 Unique contributions
The missing link in extant literature through exhaustive literature review has been identified. The present study attempts to provide a sustainable transportation framework for Indian road and shipping sector. Moreover, this study contributes to the literature by investigating the impact of government policy, economic conditions, social dimensions, green technology adoption, legal factors and environmental dimensions on sustainable transportation development. To our knowledge, this is the first effort to develop a framework for Indian shipping and road transportation sector and empirically validate it based on response collected from 100 firms that represent Indian shipping, road transporters and freight forwarders.
5.2 Managerial implications
Many of our present study findings offer guidance to shipping and road transportation companies. The GESTLE framework for sustainable transportation clearly highlights the role of economic, social, technology, legal and environment. The managers must leverage the economic situation, social dimensions, legal structure and environmental conditions to implement sustainable transportation. We recognise that the idea of recommending organisations to actively expose themselves to macro factors sounds rational based on our findings; however to successfully assimilate the impact of macro factors, the macro factors are not enough to impact the sustainable transportation. Further, this discussion has been extended in our next section.
6 Conclusions
Drawing broadly from GESTLE impacts on sustainable transportation, and the extant literature, a conceptual framework was developed and tested. The conceptual framework reconciles the independent contributions of two well established streams in literature:
20 R. Dubey and A. Gunasekaran
studies that explain the impact of GESTLE framework on sustainable transportation and those to sustainable shipping and transportation. The analyses based on 100 respondents from Indian shipping and trucking firms, support the hypothesised relationships model expecting government, which is not statistically supported.
6.1 Research limitations
Like every study, our present study also has its own limitations. First, our study does not include aviation and rail sector. The aviation has severe impact on climate change and the rate at which the density of air traffic is increasing, requires a dedicated study. Second, in our study, the demographic profiles of the respondents or nature of the industry (i.e., road or shipping) have not been controlled. Third, our model only includes macro factors. However, the micro factors like shipping equipment or shipping documentation could have yielded different insight. Last, a very important aspect that may impact the outcome of the study is data collection at one point of time. Consequently, the causality cannot be established without longitudinal data.
6.2 Further research directions
The limitations of our present study can further be extended in future. The future research directions are outlined as:
• There is need for sustainable transportation framework for road and shipping sectors, separately as their scope of operations, size and organisational structure are greatly different.
• The present study needs to be further investigated under the light of soft dimensions (i.e., the impact of organisational culture, the level of collaboration among different members of supply chain network and role of leadership in organisation).
• An attempt can be made in future to investigate the impact of human resource practices on sustainable shipping and trucking sector.
• The present study can be further investigated from the light of training need for truck drivers and seafarers from sustainability point of view. In one of the studies, Jabbour and Jabbour (2014) proposed a low carbon training framework for operations and production. However, the study can further be extended to address the sustainability issues through truck drivers training and seafarers.
• Our study has revealed corruption practices as an important component of social dimension. However, the extant literature has failed to address corruption practices and its impact on sustainable transportation. In future, addressing this untouched issue can be recommended.
• In future, academicians can come out with special issues focusing on the need for training in sustainable supply chain network, and the role of seafarers on sustainable transportation and logistics.
• In this way, present study can be concluded. Moreover, our framework needs to be tested in different countries to further generalise it.
Sustainable transportation 21
Acknowledgements
The authors would like to thank the anonymous reviewers for their constructive and helpful comments which helped to improve the presentation of the paper considerably.
References Acciaro, M. (2014) ‘A real option application to investment in low-sulphur maritime transport’,
International Journal of Shipping and Transport Logistics, Vol. 6, No. 2, pp.189–212. Amekudzi, A.A., Jotin Khisty, C. and Khayesi, M. (2009) ‘Using the sustainability footprint model
to assess development impacts of transportation systems’, Transportation Research Part A: Policy and Practice, Vol. 43, No. 4, pp.339–348.
Anderson, J.C. and Gerbing, D.W. (1988) ‘Structural equation modeling in practice: a review and recommended two-step approach’, Psychological Bulletin, Vol. 103, No. 3, p.411.
Armstrong, J.S. and Overton, T.S. (1977) ‘Estimating nonresponse bias in mail surveys’, Journal of Marketing Research (JMR), Vol. 14, No. 3, pp.396–402.
Bai, C. and Sarkis, J. (2010) ‘Greener supplier development: analytical evaluation using rough set theory’, Journal of Cleaner Production, Vol. 17, No. 2, pp.255–264.
Bichou, K., Lai, K.H., Lun, Y.V. and Cheng, T.C.E. (2007) ‘A quality management framework for liner shipping companies to implement the 24-hour advance vessel manifest rule’, Transportation Journal, Vol. 46, No. 1, pp.5–21.
Chalk, S.G. and Miller, J.F. (2006) ‘Key challenges and recent progress in batteries, fuel cells, and hydrogen storage for clean energy systems’, Journal of Power Sources, Vol. 159, No. 1, pp.73–80.
Chin, W.W. (1998) ‘The partial least squares approach to structural equation modeling’, Modern Methods for Business Research, Vol. 295, No. 2, pp.295–336.
Churchill, G.A. Jr. (1979) ‘A paradigm for developing better measures of marketing constructs’, Journal of Marketing Research, Vol. 16, No. 1, pp.64–73.
Curran, P.J., West, S.G. and Finch, J.F. (1996) ‘The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis’, Psychological Methods, Vol. 1, No. 1, pp.16–19.
Dillman, D. (2007) Mail and Internet Surveys: The Tailored Design Method, Wiley, New York. Doudnikoff, M., Gouvernal, E. and Lacoste, R. (2014) ‘The reduction of ship-based emissions:
aggregated impact on costs and emissions for North Europe-East Asia liner services’, International Journal of Shipping and Transport Logistics, Vol. 6, No. 2, pp.213–233.
Eyring, V., Isaksen, I.S., Berntsen, T., Collins, W.J., Corbett, J.J., Endresen, O. and Stevenson, D.S. (2010) ‘Transport impacts on atmosphere and climate: shipping’, Atmospheric Environment, Vol. 44, No. 37, pp.4735–4771.
Fornell, C. and Cha, J. (1994) ‘Partial least squares’, in Bagozzi, R.P. (Ed.): Advanced Methods of Marketing Research, pp.52–78, Basil Blackwell, Cambridge, MA.
Fornell, C. and Larcker, D.F. (1981) ‘Evaluating structural equation models with unobservable variables and measurement error’, Journal of Marketing Research (JMR), Vol. 18, No. 1, pp.39–50.
Frynas, J.G. (2012) ‘Corporate social responsibility or government regulation? Evidence on oil spill prevention’, Ecology & Society, Vol. 17, No. 4, p.4.
Glover, J.L., Champion, D., Daniels, K.J. and Dainty, A.J.D. (2014) ‘An institutional theory perspective on sustainable practices across the dairy supply chain’, International Journal of Production Economics, Vol. 152, pp.102–112.
Goldemberg, J. (2006) ‘The promise of clean energy’, Energy Policy, Vol. 34, No. 15, pp.2185–2190.
22 R. Dubey and A. Gunasekaran
Golicic, S.L., Boerstler, C.N. and Ellram, L.M. (2010) ‘‘Greening’ transportation in the supply chain’, MIT Sloan Management Review, Vol. 51, No. 2, pp.47–55.
Gray, P.G. and Frost, J.C. (1998) ‘Impact of catalysis on clean energy in road transportation’, Energy & Fuels, Vol. 12, No. 6, pp.1121–1129.
Greene, D.L. and Wegener, M. (1997) ‘Sustainable transport’, Journal of Transport Geography, Vol. 5, No. 3, pp.177–190.
Guerrero, S.E., Madanat, S.M. and Leachman, R.C. (2013) ‘The trucking sector optimization model: a tool for predicting carrier and shipper responses to policies aiming to reduce GHG emissions’, Transportation Research Part E: Logistics and Transportation Review, Vol. 59, pp.85–107.
Haines, A., Smith, K.R., Anderson, D., Epstein, P.R., McMichael, A.J., Roberts, I. and Woods, J. (2007) ‘Policies for accelerating access to clean energy, improving health, advancing development, and mitigating climate change’, The Lancet, Vol. 370, No. 9594, pp.1264–1281.
Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1998) Multivariate Data Analysis, Prentice-Hall International, Inc., Upper Saddle River, New Jersey.
Hofstede, G. (1986) ‘Cultural differences in teaching and learning’, International Journal of Intercultural Relations, Vol. 10, No. 3, pp.301–320.
Hooper, P. (1997) ‘Liberalising competition in domestic airline markets in Asia – the problematic interface between domestic and international regulatory policies’, Transportation Research Part E: Logistics and Transportation Review, Vol. 33, No. 3, pp.197–209.
Jabbour, C.J.C. and Jabbour, A.B.L.S. (2014) ‘Low-carbon operations and production: putting training in perspective’, Industrial and Commercial Training, Vol. 46, No. 6, p.6.
Konur, D. and Schaefer, B. (2014) ‘Integrated inventory control and transportation decisions under carbon emissions regulations: LTL vs. TL carriers’, Transportation Research Part E: Logistics and Transportation Review, Vol. 68, pp.14–38.
Lai, K.H., Lun, Y.V., Wong, C.W. and Cheng, T.C.E. (2013) ‘Measures for evaluating green shipping practices implementation’, International Journal of Shipping and Transport Logistics, Vol. 5, No. 2, pp.217–235.
Lambert, D.M. and Harrington, T.C. (1990) ‘Measuring nonresponse bias in customer service mail surveys’, Journal of Business Logistics, Vol. 11, No. 2, pp.5–25.
Litman, T. (2007) ‘Developing indicators for comprehensive and sustainable transport planning’, Transportation Research Record: Journal of the Transportation Research Board, Vol. 2017, No. 1, pp.10–15.
Litman, T. and Burwell, D. (2006) ‘Issues in sustainable transportation’, International Journal of Global Environmental Issues, Vol. 6, No. 4, pp.331–347.
Lu, C.S., Kuo, S.Y. and Chiu, Y.T. (2013) ‘Ethical leadership and ethical climate in the container shipping industry’, International Journal of Shipping and Transport Logistics, Vol. 5, No. 6, pp.591–604.
Maes, F. (2008) ‘The international legal framework for marine spatial planning’, Marine Policy, Vol. 32, No. 5, pp.797–810.
Maritime Administration, US Department of Transportation (2008) Impact of High Oil Prices on Freight Transportation: Modal Shift Potential in Five Corridors [online] http://www.marad.dot.gov/documents/Modal_Shift_Study_-_Executive_Summary.pdf (accessed 11 June 2014).
May, T. and Crass, M. (2007) ‘Sustainability in transport: implications for policy makers’, Transportation Research Record: Journal of the Transportation Research Board, Vol. 2017, No. 1, pp.1–9.
McKinnon, A. and Kreie, A. (2010) ‘Adaptive logistics: preparing logistical systems for climate change’, Logistics Research Network Conference in Harrogate, September, pp.8–10.
Meena, P.L. and Sarmah, S.P. (2012) ‘Development of a supplier satisfaction index model’, Industrial Management & Data Systems, Vol. 112, No. 8, pp.1236–1254.
Sustainable transportation 23
Midilli, A., Dincer, I. and Ay, M. (2006) ‘Green energy strategies for sustainable development’, Energy Policy, Vol. 34, No. 18, pp.3623–3633.
Mohammadi, M., Torabi, S.A. and Tavakkoli-Moghaddam, R. (2014) ‘Sustainable hub location under mixed uncertainty’, Transportation Research Part E: Logistics and Transportation Review, Vol. 62, pp.89–115.
Murphy, P.R. and Poist, R.F. (2003) ‘Green perspectives and practices: a ‘comparative logistics’ study’, Supply Chain Management: An International Journal, Vol. 8, No. 2, pp.122–131.
Nealer, R., Weber, C.L., Hendrickson, C. and Scott Matthews, H. (2011) ‘Modal freight transport required for production of US goods and services’, Transportation Research Part E: Logistics and Transportation Review, Vol. 47, No. 4, pp.474–489.
Nguene, G., Fragnière, E., Kanala, R., Lavigne, D. and Moresino, F. (2011) ‘SOCIO-MARKAL: integrating energy consumption behavioral changes in the technological optimization framework’, Energy for Sustainable Development, Vol. 15, No. 1, pp.73–83.
OECD (1976) Measuring Social Well-Being: A Progress Report on the Development of Social Indicators, OECD Publications, Paris.
OECD (1982) The OECD List of Social Indicators, OECD Publications, Paris. Qi, X. and Song, D.P. (2012) ‘Minimizing fuel emissions by optimizing vessel schedules in
liner shipping with uncertain port times’, Transportation Research Part E: Logistics and Transportation Review, Vol. 48, No. 4, pp.863–880.
Richardson, B.C. (2005) ‘Sustainable transport: analysis frameworks’, Journal of Transport Geography, Vol. 13, No. 1, pp.29–39.
Ringle, C.M., Wende, S. and Will, S. (2005) SmartPLS 2. (M3) Beta, University of Hamburg [online] http://www.smartpls.de (accessed 10 May 2014).
Šakalys, A. and Palšaitis, R. (2006) ‘Development of intermodal transport in new European Union states’, Transport, Vol. 21, No. 2, pp.148–153.
Sarkis, J., Zhu, Q. and Lai, K. (2011) ‘An organizational theoretic review of green supply chain management literature’, International Journal of Production Economics, Vol. 130, No. 1, pp.1–15.
Shafiee, S. and Topal, E. (2009) ‘When will fossil fuel reserves be diminished?’, Energy Policy, Vol. 37, No. 1, pp.181–189.
Sorrentino, M., Rizzo, G. and Sorrentino, L. (2014) ‘A study aimed at assessing the potential impact of vehicle electrification on grid infrastructure and road-traffic greenhouse emissions’, Applied Energy, Vol. 120, pp.31–40.
Steg, L. and Gifford, R. (2005) ‘Sustainable transportation and quality of life’, Journal of Transport Geography, Vol. 13, No. 1, pp.59–69.
Tenenhaus, M., Vinzi, V.E., Chatelin, Y.M. and Lauro, C. (2005) ‘PLS path modeling’, Computational Statistics & Data Analysis, Vol. 48, No. 1, pp.159–205.
Toleman, R. and Rose, G. (2008) ‘Partnerships for progress: Toward sustainable road systems’, Transportation Research Record: Journal of the Transportation Research Board, Vol. 2067, No. 1, pp.155–163.
United Nations Development Programme (UNDP) (2011) Sustainability and Equity: A Better Future for All, Human Development Report 2011.
Vieira, J., Moura, F. and Manuel Viegas, J. (2007) ‘Transport policy and environmental impacts: the importance of multi-instrumentality in policy integration’, Transport Policy, Vol. 14, No. 5, pp.421–432.
Vlek, C. and Steg, L. (2007) ‘Human behavior and environmental sustainability: problems, driving forces, and research topics’, Journal of Social Issues, Vol. 63, No. 1, pp.1–19.
Wetzels, M., Odekerken-Schröder, G. and Van Oppen, C. (2009) ‘Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration’, MIS Quarterly, Vol. 33, No. 1, pp.177–195.
24 R. Dubey and A. Gunasekaran
Windecker, A. and Ruder, A. (2013) ‘Fuel economy, cost, and greenhouse gas results for alternative fuel vehicles in 2011’, Transportation Research Part D: Transport and Environment, Vol. 23, pp.34–40.
Wold, H. (1975) ‘Path models with latent variables: the NIPALS approach’, in Blalock, H.M., Aganbegian, A., Borodkin, F.M., Boudon, R. and Capecchi, V. (Eds.): Quantitative Sociology: International Perspectives on Mathematical and Statistical Modeling, pp.307–357, Academic, NewYork.
Wong, C.W., Lai, K.H., Shang, K.C., Lu, C.S. and Leung, T.K.P. (2012) ‘Green operations and the moderating role of environmental management capability of suppliers on manufacturing firm performance’, International Journal of Production Economics, Vol. 140, No. 1, pp.283–294.
World Economic Forum/Accenture (2009) Supply Chain Decarbonisation: The Role of Logistics and Transport in Reducing Supply Chain Carbon Emissions’, Geneva.
Yang, C.C. (2012) ‘The effect of environmental management on environmental performance and firm performance in Taiwanese maritime firms’, International Journal of Shipping and Transport Logistics, Vol. 4, No. 4, pp.393–407.
Yang, M. and Hu, G. (2013) ‘Market competition and social welfare analysis for E10 and E85 with a game theory model’, International Journal of Sustainable Economy, Vol. 5, No. 4, pp.385–403.
Yi, F., Lin, C.Y.C. and Thome, K. (2013) ‘An analysis of the effects of government subsidies and the renewable fuels standard on the fuel ethanol industry: a structural econometric model’, 2013 Annual Meeting, 4–6 August, No. 150224, Agricultural and Applied Economics Association, Washington, DC.
