Antonino Marvuglia, Enrico Benetto, Beniamino Murgante

1Luxembourg Institute of Science and Technology （LIST）, ERIN - Environmental Research & Innovation Department, 41, rue du Brill, L-4422 Belvaux, Luxembourg

2School of Engineering, University of Basilicata, 10 Viale dell'Ateneo Lucano, 85100, Potenza, Italy

?

Calling for an Integrated Computational Systems Modelling Framework for Life Cycle Sustainability Analysis

Antonino Marvuglia1,?, Enrico Benetto1, Beniamino Murgante2

1Luxembourg Institute of Science and Technology （LIST）, ERIN - Environmental Research & Innovation Department, 41, rue du Brill, L-4422 Belvaux, Luxembourg

2School of Engineering, University of Basilicata, 10 Viale dell'Ateneo Lucano, 85100, Potenza, Italy

Submission Info

Communicated by Zhifeng Yang

Accepted 23 August 2015

Available 1 October 2015 Keywords

Quantitative sustainability assessment

Computational sustainability

Environmental informatics

Life Cycle Sustainability Analysis

The implementation process of sustainable development goals poses considerable challenges to policy makers, as well as to scientists. This requires improving the knowledge on linkages between the key types of resources, the resources and the environment and the resources and the economy. The underlying challenges are inherently integrated and shall be pursued in combination. This calls for an integrated approach, which still suffers of a lack of cohesion in quantitative sustainability assessment tools and cooperation among different disciplines. A conceptual framework for life cycle sustainability analysis （LCSA） which could embrace life cycle thinking methods, stakeholders analysis supported by multicriteria decision analysis （MCDA） and dynamic system modelling has been advanced by some authors, but it still lacks of operationalization. This is the right moment to tackle this challenge because of the shared consensus on the need, added value and feasibility of creating new LCSA methodologies and tools to derive consumer information, supply chain improvements and policy support. This special issue does not present a solution for an integrated operational framework, however represents an attempt to enhance cooperation of scientists belonging to different disciplines under the overarching umbrella of a lifecycle based perspective.

? 2015 L&H Scientific Publishing, LLC. All rights reserved.

1 Introduction

The assessment of solutions for reducing the environmental impact of anthropic systems, the guidance towards long term sustainable development and the support of efforts to tackle planetary boundaries are recognized priorities （Dye and McNutt, 2008； Helliwell et al., 2013； United Nations, 2014）. The urgency in developing integrative tools and methods to support policies, regulations and practices for constraint-based sustainability, both at national and international levels have been stressed in various sustainability-related studies（Heijungs et al., 2010； Rotmans, 2006）.

In （Onat et al., 2014） the so called triple bottom line （TBL） approach （UNEP-SETAC Lifecycle Initiative,2011） is promoted, which merges comprehensive social and economic indicators with the input-output （I-O）accounts. This integration can certainly favor an integrated decision-making on the triple bottom line of sustainable development, but it does not offer yet an integrated framework for computational life cycle sustainability assessment. （Jeswani et al., 2010） and （Sala et al., 2013） recognize the limitation of life cycle assessment （LCA） compared to a more extended framework for sustainability assessment （SA） which appears as very much needed. In fact, operational frameworks for lifecycle sustainability analysis （LCSA） are necessary to support the corresponding business and policy decision making processes.

It is not by chance that life cycle assessment （LCA） has now steadily moved from product-based assessments to the evaluation of systems and large scale policies and strategic decisions. These decisions have impacts on ecosystems and human well-being, which ultimately represent also a threat to the economy. The private sector is calling for LCSA methods when strategic decisions about technology development and implementation are at stake （EU, 2013）. Public policy makers need instruments to elaborate effective policies, dealing e.g. with sustainable mobility and construction, and then to measure progress （Lu et al., 2015）. The underlying challenges are inherently integrated and shall be pursued in combination （SDSN, 2014）. That is why an integrated approach is essential. This is the right moment to tackle this challenge because of the shared consensus on the need, added value and feasibility of creating new LCSA methodologies and tools to derive consumer information, supply chain improvements and policy support （European Commission, 2011）. A conceptual framework for LCSA has already been proposed （Guinée et al., 2011； Halog and Manik, 2011）, combining inputs from different disciplines which synergistically bring their knowledge bases and their solutions to analytical models. This framework, however, still lacks of operationalization and full integration and poses numerous challenges, as well as numerous opportunities （Hellweg and Milà i Canals, 2014）. Last but not least, in the era of the fourth paradigm of science （Hey et al., 2009）, numerous challenges and opportunities are offered to the sustainability science by the advent of big data （Cooper et al., 2013）.

We share here the view of （Cucurachi and Suh, 2015）, who call for an increased cohesion in Quantitative Sustainability Assessment （QSA）. They highlighted three fundamental needs for QSAs to monitor the progresses of sustainable development goals （SDGs） and to support decision-makers. These are： （1） the use of QSAs in a multicriteria setting, （2） the embracing of causal relationships in the assessment, and （3） the adequate characterization of uncertainty of QSAs' results.

We can also argue that QSA is ultimately the result of what can be otherwise called, in a larger sense, Computational Sustainability （CS）. CS is an interdisciplinary field that aims to apply techniques from computer and information science （e.g., cloud computing, machine learning） and related disciplines （e.g., operations research, management science, applied mathematics, statistics） to the balancing of environmental, economic, and societal needs, in pursuit of sustainable development （Carla, 2009）. The focus of CS is on developing computational and mathematical models, methods, and tools for decision making concerning the management and allocation of resources for sustainable development （Carla, 2009）.

The same need for an inter-disciplinary approach advocated by （Cucurachi and Suh, 2015） is highlighted by （Carla, 2009）： “sustainable development is a complex, multi-dimensional phenomenon, with a breadth and depth that cannot be fully covered by the current portfolio of reductionist-oriented tools. We therefore need a new generation of modelling tools that can （semi-） quantitatively assess at least the triple dimensions of sustainable development, in terms of multiple scales, multiple domains and multiple generations”.

In other words, a comprehensive Computational Systems Modelling Framework for Life Cycle Sustainability Analysis is the next important priority of sustainability science.

This special issue is the second of two volumes of the Journal of Environmental Accounting and management dedicated to “Computational algorithms for Sustainability Assessment”. They come as one of the results of the workshop bearing the same name, organized on the 30th of June 2014 at the University of Minho（Guimar?es, Portugal） in the framework of the 2014 International Conference on Computational Science and Applications （ICCSA2014）. Submissions were extended to the scientific community at large.

The issue shows a series of concrete examples of application of application of computational techniques either with a direct link to the LCA area, or with a clear potential to improve the LCSA framework.

2 Summary of the special issue

The issue is a collection of seven papers. The first paper, "Coupling Multi-objective Constrained Optimization, Life Cycle Assessment, and De-tailed Process Simulation for Potable Water Treatment Chains", by F. Capitanescu et al., represents a sophistication of a basic LCA model, to deal with the optimization of an industrial system under different constraints. It proposes a multi-objective constrained optimization approach which trades-off operational costs and environmental impacts, while satisfying outlet water drinkability criteria in a potable water production plant. The proposed eco-design optimization approach is illustrated on a real-world model of a water production plant. The paper compares the relative performances of two highly praised state-of-the-art derivative-free global optimization algorithms, namely the Strength Pareto Evolutionary Algorithm （SPEA2） and Non-dominated Sorting Genetic Algorithm （NSGA-II）.

The second paper, "Random Forest for Toxicity of Chemical Emissions： Features Selection and Uncertainty Quantification", by A. Marvuglia et al., deals with the selection of informative variables in the problem of deriving characterization factors for eco-toxicology and human toxicology of chemical compounds starting from molecular-based properties. The Random Forest algorithm has been applied to single out the most relevant variables when modelling one toxicity factor at the time.

The third paper, "A Geospatial approach to determine Lake Depth and Configuration of Reingkhyongkine（Pukur Para） Lake, Rangamati Hill District, Bangladesh with Multi-Temporal Satellite data", by B. Nath et al., shows the powerful use of geospatial analysis techniques to process remotely sensed images and derive and in-depth analysis of the geomorphological dynamics of the Reingkhyongkine lake in Bangladesh.

The fourth paper, “Emergy and Form： Accounting Principles for Recycle Pathways”, by M.T. Brown, presents some new reflections about the concept of Emergy and links them to the idea of recycle pathways in such a way as to clarify emergy accounting for products and by-products. As pointed out be the author, these considerations are important when evaluating “circular economies” where products and by-products are recycled. This proposed method of dealing with recycled material is a refinement of the emergy accounting procedure.

The fifth paper, "Soil Remediation Environmental Decision Support System Based on AHPPROMETHEE II Approach", by X. Tao et al., presents an Environmental Decision Support System （EDSS）for identifying optimum soil remediation approaches for contaminated site. The authors applied the system to a case study aimed at selecting the best option of soil remediation technology for an arsenic contaminated site.

The sixth paper, "Evaluation Method for Science and Technology Performance on Energy Conservation and Emission Reduction", by L-J. Zhao et al., presents a case study based on 31 provinces of China from 2006 to 2012, to identify the role and situation of science and technology in the Energy Conservation and Emission Reduction （ECER） evaluation method. The ECER evaluation method was based on the data envelopment analysis （DEA） methodology and other auxiliary statistical methods.

The seventh paper, "Industrial Ecology opportunities between CHP and Arable Farming in Alloa, Scotland", by K. Whiting and L.G. Carmona, addresses the feasibility for industrial ecology application at Alloa, Clackmannanshire, Scotland, between a consortium of arable farmers and a waste-to-energy company. The authors identified that the heat, carbon dioxide and fertilizer produced by the company （foreseeing to operate 10 MW combined heat and power -CHP- plant） could be utilized by the consortium to produce higher quality food products in a symbiotic manner.

Carla P. Gomes （2009）, Computational Sustainability： Computational Methods for a Sustainable Environment, Economy, and Society. The Bridge， Fronteers of engineering, 39, 5-13.

Cooper, J., Noon, M., Jones, C., Kahn, E., Arbuckle, P. （2013）, Big Data in Life Cycle Assessment. Journal of Industrial Ecology, 17, 796-799.

Cucurachi, S., Suh, S. （2015）. A Moonshot for Sustainability Assessment, Environmental Science & Technology. doi：10.1021/acs.est.5b02960

自動(dòng)駕駛在早些年前開(kāi)始便得到了人們空前的關(guān)注，也能夠在今日得以空前發(fā)展。它帶給社會(huì)的不只是技術(shù)，也不僅是資金，更是全球性人工智能的發(fā)展，更是人們價(jià)值觀的一種提升，更是道德淪理的一種上升。所以技術(shù)是工程師提供，技術(shù)背后的一切體驗(yàn)都將賦予于人們，這從某種程度上講，這場(chǎng)技術(shù)性的革命是人類(lèi)歷史發(fā)展歷程上一次跨紀(jì)元的成功。

Dye, C., McNutt, M. （2008）, The Science of Sustainability, Science, 1499.

EU （2013）, Sustainable process industry. Multi-annual roadmap for the contractual PPP under Horizon 2020. European Commission, Luxembourg.

European Commission （2011）, Analysis associated with the Roadmap to a Resource Efficient Europe - Part II.

Guinée, J.B., Heijungs, R., Huppes, G., Zamagni, A., Masoni, P., Buonamici, R., Ekvall, T., Rydberg, T. （2011）, Life Cycle Assess-ment： Past, Present, and Future, Environmental Science & Technology, 45, 90-96.

Halog, A., Manik, Y. （2011）, Advancing Integrated Systems Modelling Framework for Life Cycle Sustainability Assessment, Sustainability, 3, 469-499.

Heijungs, R., Huppes, G., Guinée, J.B. （2010）. Life cycle assessment and sustainability analysis of products, materials and technologies. Toward a scientific framework for sustainability life cycle analysis, Polymer Degradation and Stability, 95, 422-428.

Helliwell, J., Layard, R., Sachs, J. （2013）. World happiness report. United Nations Sustainable Development Solutions Network.

Hellweg, S., Milà i Canals, L. （2014）. Emerging approaches, challenges and opportunities in life cycle assessment, Science, 344, 1109-1113.

Hey, T., Tansley, S., Tolle, K. （2009）. The Fourth Paradigm： Data-Intensive Scientific Discovery. Microsoft Research, Redmond, Washington.

Jeswani, H.K., Azapagic, A., Schepelmann, P., Ritthoff, M. （2010）. Options for broadening and deepening the LCA approaches, Journal of Cleaner Production, 18, 120-127.

Lu, Y., Nakicenovic, N., Visbeck, M., Stevance, A.-S. （2015）. Policy： Five priorities for the UN Sustainable Development Goals, Nature, 520, 432-433.

Onat, N., Kucukvar, M., Tatari, O. （2014）. Integrating triple bottom line input-output analysis into life cycle sustainability assessment framework： the case for US buildings, The International Journal of Life Cycle Assessment, 19, 1488-1505.

Rotmans, J. （2006）. Tools for Integrated Sustainability Assessment： A two-track approach, The Integrated Assessment Journal, 6, 35-57.

Sala, S., Farioli, F., Zamagni, A. （2013）. Progress in sustainability science： lessons learnt from current methodologies for sustainability assessment： Part 1. The International Journal of Life Cycle Assessment, 18, 1653-1672.

SDSN （2014）. An Action Agenda for Sustainable Development. SDSN - United Nations Sustainable Development Solutions Network.

UNEP-SETAC Lifecycle Initiative, 2011. Towards a Life Cycle Sustainability Assessment. UNEP.

United Nations （2014）. Report of the Open Working Group of the General Assembly on Sustainable Development Goals （No. A/68/970）.

20 August 2015

.

Email address： antonino.marvuglia@list.lu

ISSN 2325-6192, eISSN 2325-6206/$- see front materials ? 2012 L&H Scientific Publishing, LLC. All rights reserved.

10.5890/JEAM.2015.03.000