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Journal of Cleaner Production 53 (2013) 1e6

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Journal of Cleaner Production

journal homepage: www.elsevier .com/locate/ jc lepro

Risk management in cleaner production

Desheng Dash Wu a,b,*, David L. Olson c, John R. Birge d

aRiskLab, University of Toronto, Toronto, ON M5S 3G3, CanadabDepartment of Engineering Management and Financial Engineering, Reykjavik University, Menntavegur 1, 101 Reykjavik, IcelandcCollege of Business Administration, University of Nebraska-Lincoln, Lincoln, NE 68588-0491, USAdBooth School of Business, The University of Chicago, Chicago, IL 60637-1610, USA

a r t i c l e i n f o

Article history:Received 6 February 2013Accepted 7 February 2013Available online 27 February 2013

Keywords:RiskCleaner productionModelsRisk reductionRisk prevention

* Corresponding author. RiskLab, University of TorCanada.

E-mail address: dash@risklab.ca (D.D. Wu).

0959-6526/$ e see front matter � 2013 Elsevier Ltd.http://dx.doi.org/10.1016/j.jclepro.2013.02.014

a b s t r a c t

This special volume of the Journal of Cleaner Production addresses risk issues in process managementrelated to cleaner production and green production. Risks arise in everything humans attempt. Doingbusiness has no profit without risk, rewarding those who best understand systems and take what turnsout to be the best way to manage risks. The editorial team and the authors of the papers of this specialvolume have highlighted general risks common to a number of different production contexts. Riskmanagement is addressed as applied to production in the food we eat, the energy we use to live, and themanifestation of the global economy via supply chains. Models are reviewed as a means to quantify risks,and to aid in decision-making concerning ways to reduce the risks in such complex interactions.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Living and working in today’s environment involves many risks,ranging from production activities including but not limited tomanufacturing, mining, energy production, transmission and us-age, chemicals, new technologies, food, and supply chain trans-portation. The tools and procedures used to make decisions inregard to the risks associated must consider both the need to keeppeople gainfully and safely employed through increased economicactivity and to protect the earth’s ecosystems from threats arisingfrom human activities. We need to consider that there are manyrisks and we have challenges to develop strategies, controls, andregulations designed to reduce the risks while seeking to achieveother goals.

There has been a great deal of research published on risk anal-ysis, including many articles in the Journal of Cleaner Production.Case studies have been presented to identify such issues inmanufacturing automobiles (Jasch and Lavicka, 2006; Lee, 2011;Autry and Bobbitt, 2008). These studies reviewed risk factors anddemonstrated processes to gather risk data that could be used totake action to reduce and/or to prevent the risks.

onto, Toronto, ON M5S 3G3,

All rights reserved.

This special volume focuses on risk management in productionoperations. It includes:

a. case study to demonstrate the practical application of the lifecycle assessment method with reference to the buildingpractices for all life cycle stages in Hong Kong titled “Envi-ronmental Emissions and Energy Consumptions Assessment ofa Diesel Engine from the Life Cycle Perspective” by Liu et al.(2013).

b. a modeling paper for environmental risk management inTaiwan titled, ‘Trace Anthropogenic Arsenic in Taiwan e Sub-stance Flow Analysis as a Tool for Environmental Risk Man-agement’ by Chen et al., (2013).

c. a methodology paper titled, ‘Main Exposure Scenarios in theproduction of CNT-Polymer Nanocomposites by Melt-MoldingProcess,’ by Fleury et al. (2013).

d. a paper, titled, ‘Risk Reduction through Early Assessment andIntegration of Sustainability in Design in theMinerals Industry,’based upon a case study of risk reduction through earlyassessment and integration of sustainability in design in theminerals industry by McLellan and Corder (2013).

e. a policy analysis paper titled, ‘Policy Risks in Planning theHousing Product Process: A Holistic Perspective,’ about policyrisks in developing the housing product process from a holisticperspective by Zhang et al. (2013).

f. a comparative study for two types of solid waste incinerationtechnologies, the fluidized-bed incinerator and the mechanical-

D.D. Wu et al. / Journal of Cleaner Production 53 (2013) 1e62

grate incinerator titled “Comparative streamlined life cycleassessment for two types of municipal solid waste incinerator”by Ning et al. (2013).

g. a book review and short communication paper on extreme-event risk management based on a white report “Lee, B.,Preston, F. 2012. Preparing for High-impact, Low-probabilityEvents: Lessons from Eyjafjallaj}okull. London: ChathamHouse.” By Olson and Wu (2013).

This introductory article is structured as follows: The Editorialteam focused upon risk management’s relation to sustainable andcleaner production in various contexts in Section 2. Models areaddressed in Section 3, which have been developed to study riskfactors, intended to increase knowledge of cause and effect, thusenabling better policy selection. A review of the controls that havebeen implemented in efforts to improve sustainability and cleanerproduction is included in Section 4. Section 5 contains theconclusions.

2. Contexts of sustainable production risk

Risks arise from every endeavor, which humans attempt. Life isworthwhile because of its challenges. Doing business has no profitwithout risk, rewarding those who best understand systems andtake what turns out to be the best way to manage these risks. Theeditorial team addressed riskmanagement as applied to productionin the food we eat, the energy we use to live, and the manifestationof the global economy via supply chains.

2.1. What we eat

One of the major issues facing human culture is the need forquality food. Two factors need to be considered: human populationgrowth and threats to the environment. We have understood, sinceMalthus, that the human population cannot continue to growexponentially. Some countries, such as China, have been proactivein seeking to control their population growth. Other areas, such asEurope, are documenting decreases in population growth, probablydue to societal consensus. But other areas, which include India andAfrica, continue to experience rapid increases in population. Thismay change as these areas become more affluent (see China andEurope) (Panayotou, 1996). But there is no universally acceptableway to control the growth of human population. Thus, we expect tosee continued increase in demand for food due to increases in thepopulation and due to changes in diets, which demand more ani-mal products.

Agricultural science has been relatively effective in developingbetter strains of crops, through a number of methods, includingbioengineering and genetic science. This led to what was expectedto be a ‘green revolution,’ which originated a generation ago. Aswith all of mankind’s efforts, the best laid plans of humans involvemany complexities and unexpected consequences. North Americahas developed the means to increase production of food that isfree from many of the problems that existed a century ago.However, people from different countries are concerned aboutnew threats arising from genetically modified agricultural crops.Thus genetically modified food is another example of humanefforts which were supposed to provide improvements but arecausing new dangers, or unintended consequences, with greatdisagreement about what is the truth about short and long-termhuman health and eco-system impacts of genetically modifiedorganisms.

A third factor that complicates the food issue is the distribu-tion. North America and the Ukraine have long been fertile pro-ducing centers, generating surpluses of food. This connects to

supply chains issues that are addressed in Section 3. But thechallenge is the interconnected global human system with sur-pluses in some locations and food scarcities in others. Technically,this is a supply chain issue. But more importantly it is an eco-nomic issue of sharing food, which is based upon a series ofpolitical issues. Contemporary businesses with heavy reliance oninternational collaborative supply chains, lead to many risksarising from shipping, as well as to other factors such as politicalstability, physical security from natural disaster, piracy on thehigh seas, and changing regulations. Sustainable supply chainmanagement has become an area with pressure potentiallyapplied by governing agencies, customers, and the variouscorporate entities involved within a supply chain (Seuring andMüller, 2008). These interests can include industry cartels suchas OPEC, regulatory environments such as the Eurozone, industrylobbies as in the sugar industry, and so forth that make inter-national business on the scale induced by global supply chainscomplicated.

Water is one of the most abundant assets on the Earth, probablynext to oxygen, which chemists know is a related element. Rain-water used to be considered pure. The industrial revolution causedthe unintended production of acid precipitation with numerousunanticipated consequences, locally, regionally and globally. Waterused to be free in many places. Only 30 years ago, paying for waterwould have been considered the height of idiocy. Managing wateris recognized as a major issue in that less than 3 per cent of theworld’s water is fresh (Lambooy, 2011). Lambooy called for morework on wastewater management, management of freshwaterconsumption, and groundwater quantity and quality management.Water management is increasingly an economic issue, leading tothe political arena. Wherever water is scarce, it induces politicalefforts to gain a greater share for each political entity, involvingallocations not only for drinking water for cities, but also for agri-cultural irrigation and for maintaining sustainable levels to enableriver navigation.

2.2. The energy we use

Generation of energy, in various forms, is a major issue leadingto political debate concerning tradeoffs among those seeking toexpand extraction of fossil fuels to meet the expanding demandsvs. those who seek to help to reduce the climate change causingrelease of fossil carbon dioxide by working to make the transitionto renewable energy and a strong emphasis on reduction of in-efficiencies in the entire system. Oil continues to be a majorsource of energy, but its, extraction, refining, transportation andusage not only causes numerous environmental risks (Ng andGoldsmith, 2010) but its also causes related catastrophic riskssuch as oil spills and market risks such as highly fluctuatingprices.

Increasingly, many nations and regions within nations areexpanding efforts to transition to post fossil carbon societies bychanging to renewable energy based systems. For example, Ng andGoldsmith (2010) developed a conceptual and dynamic program-ming model to explain the entry behaviors of different types of bioenergy businesses, and to demonstrate that bio energy entry de-cisions emphasize a basic trade-off involving gains from acommitment to specialized, and correspondingly higher cost assets,and gains from remaining flexible with lower levels of fixed andless specific assets. Meyler et al. (2007) developed insights intocomplex landscapes of risk in which the natural environment andwell-being of residents was largely ignored as fuel prices and en-ergy security were debated.

The impacts of oil exploration in the Mexican rainforest werereported by Santiago (2011): urbanization and civilization were

D.D. Wu et al. / Journal of Cleaner Production 53 (2013) 1e6 3

highly localized, collapsing for reasons not well understood inNorthern Veracruz. Cost risks in alternative energy resourceswere studied by Zhelev (2005), and financial management usingthe Pinch concept have been proven to be useful in developmentof tools at the preliminary design stage for rough target setting,alternatives evaluation and decision-making. The risks involvedin gasoline blending (Mata et al., 2005) and of ethanol produc-tion and usage, were reviewed by Von Blottnitz and Curran(2007). They found that technological choices in process res-idue handling and in fuel combustion are key, whilst site-specificenvironmental management tools should address the broaderbiodiversity issues.

Mining is a field, which traditionally faces high production riskssuch as uncertain supply yield or marginal cost variability. Cyanidemanagement was addressed by Akcil (2006), reporting on practicesin gold and silver mining in Turkey.

Life cycle mine management was addressed through risk man-agement techniques (Sharratt and Choong, 2002; Fourie and Brent,2006). In these author’s papers used life-cycle assessment as a toolthat is useful for identifying potential risk issues throughout aproduct’s life-cycle and for providing qualitative/quantitative datato support the risk assessment and the risk reduction processes.

3. Models in risk management and risk reduction

Risk management and risk reduction models vary with aca-demic field. Physics and chemistry models tend to be the mostprecise in that solid models and data are used (Breivik et al.,2006). However, new technologies, such as nanotechnology,create many unknowns such as new human health risks andenvironmental risks (Knowles, 2006; Walsh et al., 2008). Theseresearchers documented, that some types of nanotechnologycreate potential adverse health effects due to three propertiescommon to most nanoparticles: (a) Small size: the particles are sosmall they can easily gain entry to the body and tissue cells; (b)Large surface to volume ratio: this implies higher reactivity onceinside the body; and (c) Solubility: not all nanoparticles are sol-uble, which may affect where they are deposited in the body.These researchers recommended, that authorities need to formu-late regulations to protect the public from possible health risksassociated with exposure to nanoparticles, and to provide a betterunderstanding of nanoparticles, which is necessary to properly,enforce regulations to control/reduce risks from the productionand usage of nanoparticles.

Dealing with business issues leads to more subjectivity,although such systems can be modeled (Evan and Hamner, 2003).Many multiple-criteria risk reduction models have been developedfor various sustainability risk contexts. Elfkih et al. (2009) devel-oped a goal-programming model for the food sector, demonstratedwith a case application to establish sensible compromises amongeconomic and environmental criteria in decisions related toreducing risks in production within irrigated agriculture in aSpanish district. The purpose was to seek compromises acceptableto all opposing interest groups. Kangas et al. (2001) applied a se-lection model in forestry planning, while Parra-López et al. (2008)used a similar model in Spanish olive production management.Both researcher teams documented that multi-criteria decision-making tools such as analytic hierarchy process (AHP) are veryuseful: the former built it into a forest management planning sys-tem, which is a tool of central importance, both in providingforestry advisory service and in public participation in forestry-related decision-making in Finland. The latter effectively utilizedit in assisting olive production management in Spain. Kangas et al.’smodel used indirect measures, to include conservation values suchas hectares of conserved area and hectares of conserved

commercial forests, as well as effects on employment and grossnational product. These factors could represent various aspects ofrisk in this forestry, planning model. They reported that suchmultiple criteria models have been applied in forestry managementplanning in Finland since the 1990s. Parra-López et al. applied ananalytic hierarchy process model to planning of sustainable olivegrowing systems in Spain. This model evaluated three alternativesystems, conventional, organic, and integrated systems with theintent of reducing environmental damage from single-cropfarming, over-use of chemicals, and over-exploitation of under-ground water.

Martinez-Cordero and Leung (2004) developed a multiplecriteria optimization model for sustainable shrimp farming, anddemonstrated that the computed results of the multi-criteriadecision-making analyses support the claim that semi-intensivefarms, which are more common in Mexico, can be managed topromote sustainability.

In the watershed management context, Merrick et al. (2005)combined input from multiple experts in a decision involving wa-ter quality. This approach, according to the authors, successfullyidentified the major problems with the watershed, provided anintegrated watershed assessment tool for the project committeeand identified opportunities for improvement.

In the energy industry, Chatzimouratidis and Pilavachi (2009)used a multiple criteria selection model to evaluate ten types ofpower plants with nine end node criteria. The evaluationwas basedon technological, economic and sustainability criteria by theapplication of the AHP and they documented that renewable en-ergy power plants are the best solution for the future.

Laurence (2006) used a risk evaluation model for mine life cyclerisk management and showed that premature and planned mineclosures can result in significant, adverse impacts to the environ-ment and the community. He presented a risk management modelto assess the risks of premature mine closures, which includedenvironmental, safety, community, land use, legal, financial, andtechnical features. Laurence applied a risk matrix to classify theexpected risk consequences of mine closure, and reported results ofapplying this approach to five mine closures through a survey. Themodel focused on the consequences of mine closures on each of thestated risk categories.

Rogers and Seager (2009) utilized a life cycle impact model tocompare alternative transportation fuels using the Greenhouse gasRegulated Emissions, and showed that the method can be used tofacilitate exploration and construction of context-specific criteriapreferences by simultaneously representingmultipleweight spacesand the sensitivity of the rank ordering to uncertain stakeholdervalues. This model allowed each stakeholder to express acceptablelevels of risk and provided a list of conflicts across stakeholders.While the method was unable to reconcile the differences ofopinion, it at least explicitly addressed such conflicts.

Lu et al. (2007) and Tsai and Hung (2009) studied multiplecriteria in evaluating a supply chain operation. Lu et al. (2007)applied an AHP framework to evaluate green suppliers. Heconsidered risks in various components of each supply chain stagefrom pre-manufacturing to the end-of-product life management.Tsai and Hung (2009) demonstrated that a fuzzy goal programmingapproach can be integrated into activity-based costing and per-formance evaluation in a value-chain structure for optimal GSCsupplier selection considering green factors in production anddistribution. Risk was incorporated into the goal, programmingmodel with goals for recycling and logistic risk factors, factors suchas on-time delivery, defect rates, and uncertainty in cost and rev-enue parameters.

Some research in sustainable production suggested replacinghuman judgment (Danihelka, 2004). However, the editors of this

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special volume believe that it is better to support and strengthenhuman judgment rather than remove it. Various tools can be usedto improve human judgment, tools such as simulation to displayexpected risks through probability, and to support selection de-cisions that are ultimately made by humans, but with assistancefrom optimization tools (Ravindran et al., 2010) or multiple criteriatools that focus on tradeoffs (Padillo and Diaby,1999). Strang (2010)provided a case study of the interaction of engineering and politicsin a nuclear applicationwith a focus on selecting a decision-makingmodel. Brainstorming and an AHP model were applied to assessproject risk in building a tritium extraction facility to providetritium for themedical industry. Experts assessed the risks involvedin the alternative manufacturing options. The intent of the modelwas to help politicians and scientific experts to consider long-termhealth and safety risks. While such a model cannot guaranteeagreement, it does enable clearer understanding of the tradeoffs.

The uncertainty inherent in risk analysis has typically been dealtwith in two ways. One is to either measure distributions or to as-sume them to normal and to apply simulation models. Rijgersberget al. (2009) used a discrete-event simulation model for assessingrisks involved in fresh-cut vegetables. The management of risks inthe interaction of food production, water resource use, and pollu-tion generation were studied by using the Monte Carlo simulationdeveloped by Sun et al. (2011). The simulation model enabledidentification of the optimal biomass facility option in ShandongProvince, considering risk reflected through uncertain parametersfor cost and price.

4. Control

Control of multiple combinations of risks has been testedthrough a number of mechanisms. For example, Labodová (2004)reported on results of Czech case studies designed to integrateseparate management systems for quality, environmental, andhealth and safety management into an integrated system. The re-sults showed that the integrated system was more effectivethan when the three systems were used separately within thecompanies.

Fernández-Muñiz et al. (2012) studied the reasons for Europeanfirms to implement occupational health and safety standards withregard to industrial accidents. They reviewed auditing standards,and surveyed 131 firms concerning their employee’s perceptionsand attitudes toward risk standards. The study concluded thatoccupational health and safety audits improvedworking conditionsthrough reducing the risk of injury due to accidents, while alsoimproving firm image, and compliance with legal obligations. Riskmanagement and risk assessment are related, but are different fromeach other. Gupta et al. (2002), argued that risk assessment focusedon the magnitude of risks while risk management processes aim toidentify the best means of dealing with various risks. The modelswe have reviewed for this paper, focused primarily on comparingrisk mitigation methods that are provided. They depend entirelyupon the risk assessment input provided by experts.

One mechanism to generate solutions to better anticipate and tomanage or reduce risks is to use participatory decision-making.Barker et al. (2010) suggested stakeholders’ participation in therisk analysis. Ryu et al. (2009) applied factor analysis and structuralequation modeling to study governance mechanisms to deal withenvironmental volatility in the manufacturing processes. Engage-ment of multiple stakeholders naturally leads to the need toaddress, multiple criteria, simultaneously. A multiple criteria deci-sion analysis model to assess factors including risk was applied tomilk futures policies in Brazil (Siqueira et al., 2008). They applied anAHP analysis to rankmilk futures contracts, including assessment of

price risk. The model was assessed as a means to increase proba-bility of success by 92 percent of farmers included in the study.

5. Conclusions

Production, like every other human activity, involves risks. Wehave tried to point out some general risks common to a number ofdifferent contexts. Food and water are essential to life, but theexponentially increasing human population stresses the foodproduction system as humans try to develop new and bettersources of food and try to cope with the pollution that humanscause in water supply. Energy is essential to production, but again,the energy system is stressed to the limit with oil productionapparently peaking, while efforts to generate energy throughalternative methods run into capacity constraints. The risks offuture carbon supply are strongly debated, as is the viability ofalternative energy sources, which don’t seem to be able to main-tain viability without governmental financial support. The risks,pro and con, of alternative energy sources are one of the mosthotly debated issues in the world today. Mining is needed toprovide the materials that are required to produce artifacts, butextraction inevitably creates pollution and the mining systemstresses political development in emerging countries. Again,strong disagreement abounds about the severity of the risks andabout the alternatives to reduce them. Production of chemicalsoffers new opportunities to do things better, but my lead to un-intended consequences. Transporting goods around the worldthrough supply chains involves high levels of risk from naturaldisaster, from political activity to include war and terrorism, frompiracy, from currency exchange rate fluctuation, from qualitycontrol issues, and many other factors. The models we havereviewed cannot reconcile the judgment applied to make de-cisions. But they can more clearly expose the tradeoffs that need tobe considered.

Models provide a means to quantity risks, and to aid in decision-making concerning issues with complex interactions. Effectivemanagement of risks inherently involves tradeoffs. Optimizationmodels may identify solutions with the greatest expected short-term profits, but these solutions also tend to have high levels ofrisk, especially in the longer-term. Simulation models enableconsideration of uncertainties, as long as they are expressed in theform of probability distributions. Multiple criteria models focus onthe analysis of tradeoffs.

Once the expected relationships between causes and effects areidentified, it is more effective to reduce and manage risk. The usualforms of management of risks tend to be based upon eitherfinancial models, or through frameworks. A few of these werereviewed in Section 4. Probably more applicable in general is aparticipatory decision-making approach reviewed in the lastparagraph of Section 4.

This Special Volume includes a number of papers addressingvarious aspects of risk management in cleaner production.McClellan and Corder (2013) described the risks associated withbasemetals extraction and policy. They found that early assessmentand consideration of sustainability in design can help the managersto significantly reduce the risks.

Chen et al., (2013) demonstrate the use of modeling to managerisk. They emphasized that the risks associated with trace anthro-pogenic arsenic in Taiwan can be monitored via substance flowanalysis (SFA) as a tool for environmental risk management. Theydocumented how the proposed methodology that integrates SFAwith exposure assessment and risk evaluation offers quick exami-nation of comprehensive risk reduction alternatives.

Means of controlling risk are also addressed. Fleury et al. (2013)developed an approach in risk analysis to identify the main

D.D. Wu et al. / Journal of Cleaner Production 53 (2013) 1e6 5

exposure scenarios in the production of CNT-polymer nano-composites, based on which some recommendations were pro-posed to lower the risks of nanoparticle exposure during melt-molding processes. McLellan and Corder (2013) examine thegeneric and specific risk reduction potential of applying asustainability-based design process through the analysis of twocase studies. These authors showed that risk reduction throughearly assessment and integration of sustainability in design makesa significant contribution to the company being able to maintainits social license to operate. Zhang et al. (2013) examined thepolicy risks existing in the process of developing housing productsin China. The authors demonstrated that policy risks have a majorimpact on the development of housing products in China and withdifferent levels of influences at different development stages.

Finally, Olson and Wu (2013) wrote a book review in a shortcommunication paper on extreme- event risk management basedon a white report “Lee, B., Preston, F. 2012. Preparing for High-impact, Low-probability Events: Lessons from Eyjafjallaj}okull.London: Chatham House.”

The body of this paper partially reviewed other articles pub-lished in diverse issues of other journals. The second objective forthis article was to specifically highlight what the articles in thisvolume contribute to the science and policies of managing andreducing or preventing risks.

Acknowledgements

This paper is supported by One Hundred Person Project of TheChinese Academy of Sciences, and partially supported by the Na-tional Natural Science Foundation of China (No. 70921001, No.70631004, No. 71073177, No. 71110107024).

References

Akcil, A., 2006. Managing cyanide: health, safety and risk management practicesat Turkey’s Ovacik gold-silver mine. Journal of Cleaner Production 14 (8),727e735.

Autry, C.W., Bobbitt, L.M., 2008. Supply chain security orientation: conceptualdevelopment and a proposed framework. International Journal of LogisticsManagement 19 (1), 42e64.

Barker, G.C., Bayley, C., Cassidy, A., French, S., Hart, A., Malakar, P.K., Maule, J.,Petkov, m, Shepherd, R., 2010. Can a participatory approach contributeto food chain risk analysis? Risk Analysis: An International Journal 30 (5),766e781.

Breivik, K., Wania, F., Muir, D.C.G., Mehran, A., Backus, S., Pacepavicius, G., 2006.Empirical and modeling evidence of the long-range atmospheric transportof decabromodiphenyl ether. Environmental Science & Technology 40 (15),4612e4618.

Chatzimouratidis, A.I., Pilavachi, P.A., 2009. Technological, economic and sustain-ability evaluation of power plants using the analytic hierarchy process. EnergyPolicy 37 (3), 778e787.

Chen, P.-C., Su, H.-J., Ma, H.-W., 2013. Trace anthropogenic arsenic in Taiwan esubstance flow analysis as a tool for environmental risk management. Journal ofCleaner Production 53, 13e21.

Danihelka, P., 2004. Subjective factors of cleaner production e parallel to riskperception? Journal of Cleaner Production 12 (6), 581e584.

Elfkih, S., Feijoo, M.L., Romero, C., 2009. Agricultural sustainable management: anormative approach based on goal programming. Journal of the OperationalResearch Society 60 (4), 534e543.

Evan, J.W., Hamner, B., 2003. Cleaner production at the Asian Development Bank.Journal of Cleaner Production 11 (6), 639e649.

Fernández-Muñiz, B., Montes-Peón, J.M., Vázquez-Ordás, C.J., 2012. Occupationalrisk management under the OHSAS 18001 standard: analysis of perceptions andattitudes of certified firms. Journal of Cleaner Production 24, 36e47.

Fleury, D., Bomfim, J.A.S., Vignes, A., Girard, C., Metz, S., Muñoz, F., R’Mili, B.,Ustache, A., Guiot, A., Bouillard, J.X., 2013. Identification of the main exposurescenarios in the production of CNT-polymer nanocomposites by melt-mouldingprocess. Journal of Cleaner Production 53, 22e36.

Fourie, A., Brent, A.C., 2006. A project-based mine closure model (MCM) for sus-tainable asset life cycle management. Journal of Cleaner Production 14 (12/13),1085e1095.

Gupta, A.K., Suresh, I.V., Misra, J., Yunus, M., 2002. Environmental risk mappingapproach: risk minimization tool for development of industrial growth centresin developing countries. Journal of Cleaner Production 10 (3), 271e281.

Jasch, C., Lavicka, A., 2006. Pilot project on sustainability management accountingwith the Styrian automobile cluster. Journal of Cleaner Production 14 (14),1214e1227.

Kangas, J., Kangas, A., Leskinen, P., Pykäläinen, J., 2001. MCDM methods in strategicplanning of forestry on state-owned lands in Finland: applications and expe-riences. Journal of Multi-criteria Decision Analysis 10 (5), 257e271.

Knowles III, E.E., 2006. Nanotechnology. Professional Safety 51 (3), 20e27.Labodová, A., 2004. Implementing integrated management systems using a risk

analysis based approach. Journal of Cleaner Production 12 (6), 571e576.Lambooy, T., 2011. Corporate social responsibility: sustainable water use. Journal of

Cleaner Production 19 (8), 852e866.Laurence, D., 2006. Optimisation of the mine closure process. Journal of Cleaner

Production 14 (3/4), 285e298.Lee, K.-H., 2011. Integrating carbon footprint into supply chain management: the

case of Hyundai Motor Company (HMC) in the automobile industry. Journal ofCleaner Production 19 (11), 1216e1223.

Li, T., Liu, Z., Zhang, H., Jiang, Q., 2013. Environmental emissions and energy con-sumptions assessment of a diesel engine from the life cycle perspective. Journalof Cleaner Production 53, 7e12.

Lu, L.Y.Y., Wu, C.H., Kuo, T.-C., 2007. Environmental principles applicable to greensupplier evaluation by using multi-objective decision analysis. InternationalJournal of Production Research 45 (18/19), 4317e4331.

Martinez-Cordero, F.J., Leung, P.S., 2004. Multicriteria decision making (MCDM)model for regional sustainable shrimp farming development in northwestMexico. Aquaculture Economics & Management 8 (3/4), 179e192.

Mata, T.M., Smith, R.L., Young, D.M., Costa, C.A.V., 2005. Environmental analysis ofgasoline blending components through their life cycle. Journal of Cleaner Pro-duction 13 (5), 517e523.

McClellan, B.C., Corder, G.D., 2013. Risk reduction through early assessment andintegration of sustainability in design in the minerals industry. Journal ofCleaner Production 53, 37e46.

Merrick, J.R.W., Parnell, G.S., Barnett, J., Garcia, M., 2005. A multiple-objective de-cision analysis of stakeholder values to identify watershed improvement needs.Decision Analysis 2 (1), 44e57.

Meyler, D., Stimpson, J.P., Cutghin, M.P., 2007. Landscapes of risk. Organization &Environment 20 (2), 204e212.

Ng, D., Goldsmith, P.D., 2010. Bio energy entry timing from a resource based viewand organizational ecology perspective. International Food & AgribusinessManagement Review 13 (2), 69e100.

Ning, S., Chang, N., Hung, M., 2013. Comparative streamlined life cycle assessmentfor two types of municipal solid waste incinerator. Journal of Cleaner Produc-tion 53, 56e66.

Olson, L.D., Wu, D., 2013. Extreme-event risk management: a review of “Lee, B.,Preston, F., 2012. Preparing for high-impact, low-probability events: Lessonsfrom Eyjafjallajókull. London: Chatham House.” Journal of Cleaner Production53, 67e68.

Padillo, J.M., Diaby, M., 1999. A multiple-criteria decision methodology for themake-or-buy problem. International Journal of Production Research 37 (14),3203e3229.

Panayotou, T., 1996. An inquiry into population resources and environment. In:Ahlburg, D.A. (Ed.), The Impact of Population Growth on Well-being in Devel-oping Countries. Springer.

Parra-López, C., Calatrava-Requena, J., de-Haro-Giménez, T., 2008. A systemiccomparative assessment of the multifunctional performance of alternative olivesystems in Spain within an AHP-extended framework. Ecological Economics 64(4), 820e834.

Ravindran, A.R., Bilsel, R.U., Wadhwa, V., Yang, T., 2010. Risk adjusted multicriteriasupplier selection models with applications. International Journal of ProductionResearch 48 (2), 405e424.

Rijgersberg, H., Tromp, S., Jacxsens, L., Uyttendaele, M., 2009. Modeling logisticperformance in quantitative microbial risk assessment. Risk Analysis 30 (1),20e31.

Rogers, K., Seager, T.P., 2009. Environmental decision-making using life cycle impactassessment and stochastic multiattribute decision analysis: a case study onalternative transportation fuels. Environmental Science & Technology 43 (6),1718e1723.

Ryu, S., Lim, Y., Hong, H., 2009. Volatile environments and interfirm governance:does trust matter? Journal of Business-to-Business Marketing 16 (4), 325e342.

Santiago, M., 2011. The Huasteca rain forest. Latin American Research Review 46,32e54.

Seuring, S., Müller, M., 2008. From a literature review to a conceptual framework forsustainable supply chain management. Journal of Cleaner Production 16 (15),1699e1710.

Sharratt, P.N., Choong, P.M., 2002. A life-cycle framework to analyse business risk inprocess industry projects. Journal of Cleaner Production 10 (5), 479e493.

Siqueira, k.B., da Silva, C.A.B., Aguiar, D.R.D., 2008. Viability of introducing milkfutures contracts in Brazil: a multiple criteria decision analysis. Agribusiness 24(4), 491e509.

Strang, K.D., 2010. Radioactive manufacturing projects and politics: scientist andpolitician normalized risk decision process. International Journal of Manage-ment & Decision Making 11 (3/4), 231e248.

D.D. Wu et al. / Journal of Cleaner Production 53 (2013) 1e66

Sun, J., Chen, J., Xi, Y., Hou, J., 2011. Mapping the cost risk of agricultural residuesupply for energy application in rural China. Journal of Cleaner Production 19(2/3), 121e128.

Tsai, W.-H., Hung, S.-J., 2009. A fuzzy goal programming approach for green supplychain optimization under activity-based costing and performance evaluationwith a value-chain structured. International Journal of Production Research 47(18), 4991e5017.

Von Blottnitz, H., Curran, M.A., 2007. A review of assessments conducted on bio-ethanol as a transportation fuel from a net energy, greenhouse gas, and

environmental life cycle perspective. Journal of Cleaner Production 15 (7),607e619.

Walsh, S., Balbus, J.M., Denison, R., Florini, K., 2008. Nanotechnology: getting it rightthe first time. Journal of Cleaner Production 16 (8/9), 1018e1020.

Zhang, X., Shen, L., Tsui, Y., 2013. Policy risks in developing the housingproduct process: a holistic perspective. Journal of Cleaner Production 53,47e55.

Zhelev, T.K., 2005. On the integrated management of industrial resources incor-porating finances. Journal of Cleaner Production 13 (5), 469e474.