The OEPI project participated in the 7th International Symposium on Environmentally Conscious Design and Inverse Manufacturing EcoDesign2011. This conference was hosted in Kioto Japan and is arranged at every second year. Topics of the conference were aligned to design for value innovation towards sustainable society e.g Global issues in EcoDesign, Social Perspectives in EcoDesign and Economics of EcoDesign. Our team member Ms. Hanna Uusitalo from KONE Corporation spoke as a plenary speaker about “KONE’s Corporate Environmental Activities and Solutions that Contribute to Creating a Positive Impact on the environment”. The other speaker from our team was Mr. Asko Koskimäki from VTT and his presentation was in technical session Sustainable Assessment and LCA and he spoke about “Ecolabelling and Design for Environment in Building Transportation System”.
Listening to the plenary speakers the key message was clear. Companies are focusing to follow through the reforms on improving energy efficiency and material efficiency and CO2 reduction generated by their operations. The ecodesign principles are extended to the design of manufacturing sites and include the complete supply chain.
Looking on the more detailed technical presentations, it was rewarding to see that lots of research work is already started globally in order to promote the enabling to continuously reduce the environmental impact of daily operations across industries and supply chains. Topics like: Sustainable Energy System, Sustainability Assessment and LCA, Green Business Design and Eco-labelling were introduced.
In the same week OEPI also participated in the International Symposium “Environmental Accounting and LCA for greening the Supply Chain in Asia” in Osaka. Our team member Ms. Hannele Tonteri spoke as invited speaker about “LCA as a tool for Design for Environment”. In this symposium, very interesting issue for OEPI rose; the development of Chinese Life Cycle database (CLCD). You can read more about this database from e.g. http://lcacenter.org/lcaxi/final/357.pdf. This database is in Chinese language, but the work is going on and hopefully in future it will be translated in English. It was discussed at the symposium that CLCD database in China and ELCD database in Europa need to work close in co-operation.
Tonteri Hannele
The U.S. Energy Information Administration (EIA), Department of Energy, on July 2010 published the International Energy Outlook 2010 report. The report presents international energy projections through 2035, including outlooks for major energy fuels and associated carbon dioxide emissions (EIA, 2010). Based on the provided data, we prepared two tables.
Table 1 and figure 1 present the ratios of renewable electricity to conventional electricity used in the world. We can directly see the huge difference between Central & South America, with an average of 83%, and all other areas of the world.
Table 2 and figure 2, show tons of Carbon produced by a person per year. The data cover the time frame from 1980 untill 2007.
Due to the increasing public awareness of environmental issues, it becomes a hot topic in governments’ and companies’ politics as well. For example, it is uncommon today to see an election campaign without a proposed environmental policy, section or targets. After a quick look on the two tables and the provided figures, we can see that governments’ policies directly affect our environment. In addition, it may have an effect on the international trade relations. For instance, if Europe reduces the GHG emission and another country do not act upon environmental standards, the business relation between Europe and that country would be hampered.
As we said, emergent social awareness or public interest in environmental issues and governmental policies affect business policies in many ways. Green IT, Green Logistics, nsurance of environmental sustainability and energy efficiency are becoming new challenges or today’s companies. Environmental legislation is exerting additional pressure.
The same holds true for the mass media and society as a whole. Our focus in this paper will be on the SMEs’ sector which is one of the biggest sectors in businesses. For example, ´he European Commission for Enterprise and Industry stated that in 2009, 20 million SMEs operated in the European Union which represents roughly 99% of all businesses (EC-E&I, 2009).
Companies that envision the future and plan in advance will get a competitive advantage in the market. People started to realize the environmental issues, and have shown the interest to know more about the environmental performance of companies before purchasing products.
Similarly, the companies also market their products with environment related slogans and details(Jamous, et al., 2011). In the near future, certain directives would be passed that only those products (from organizations) which are compliant with environmental standards will be allowed to be freely
traded, as nowadays with CE standard.
So companies that fail to follow environmental standards may risk losing the potential markets & customers. Environmental directives would not only be a benefit for the environment but also for the companies; like to use reusable materials, reduce their costs, improve the processes and make the processes flexible to accept the changes from market.
Bibliography
EC-E&I. 2009. European Commission for Enterprise and Industry. European Union. [Online] 2009. http://ec.europa.eu/enterprise/policies/sme/index_en.htm. EIA. 2010. International Energy Outlook 2010. U.S. Energy Information Administration. [Online] 2010. Office
of Integrated Analysis and Forecasting U.S. Department of Energy, Washington, DC 20585.. http://www.eia.doe.gov/oiaf/ieo/pdf/0484(2010).pdf.
Jamous, Naoum, et al. 2011. Light-weight composite environmental performance indicators (LWC-EPI) concept. [book auth.] Paulina Golinska and Marek Fertsch. [ed.] Jorge Marx Gómez. Information Technologies in Environmental Engineering, New Trends and Challenges. Berlin : Springer, 2011, Vol. 3, pp. 289-299. http://www.springerlink.com/content/j86665463452kt25/.
The Case for a Sustainability Network Solution
[This is the first part of a blog post that outlines the case for introducing a many-to-many sustainability network solution. While we start here with outlining the problems that such a solution could solve, the next part will explain the business value that it brings to the participating companies]
For the last few years, I have been researching on a wide range of sustainability-related topics, most notably around 
(spanning both sustainable supplier management and green logistics) and Sustainable Products (including issues such as product compliance, product footprinting, and design for environment).
Throughout various discussions, end user interviews, and project workshops, one thing stands out as particularly common to all of these areas. All of these are inherently inter-organizational topics, which leads to and aggravates the known challenges of data availability, quality, and reliability.
In sustainable supplier management, companies typically incorporate sustainability KPIs into the supplier qualification and assessment processes. They collect these KPIs via questionnaires from their major suppliers, score the answers, and set the relative importance of each (sub)category of performance criteria which would be used as weights in the overall suppliers’ score. The result of the supplier sustainability assessment is used to generate a ‘list of preferred suppliers’ that are considered later in operational purchasing. Also, a global high-tech manufacturing company indicated that the aggregated scores determine whether the vendor ends up in one of four strategic cooperation groups, thereby receiving more influential status in future considerations. The whole process is naturally an inter-organizational engagement; the data collection process for sustainability performance indicators is tedious, error-prone, and not easily repeatable: Customer-specific content has to be provided in multiple formats and each supplier has to provide data separately for each request. The process represents a significant resource overhead for both data requestors and providers (many companies find themselves in both positions, depending on their role in the value chain).
In green logistics, shippers and carriers alike monitor and report the CO2 emissions resulting from the transportation and warehousing of products. This is driven by market-pressure to increase operational efficiency, lower fuel consumption, and offer customers a “greener”, differentiated service. Third-party organizations are also playing a catalyst role, be them public-private initiatives such as the EPA-sponsored SmartWay program in the US or solely private such as the inter-organizational Green Freight Europe consortium. Since most shippers subcontract Logistic Service Providers (LSPs) and carriers to perform their transport operations, estimating the emissions per shipper becomes a complex, inter-organizational problem suffering from data issues. A major third-party logistics provider described to us that they are receiving every week an increasing number of requests from various clients, each requesting their tailored CO2 reports summarizing the emissions that their shipments caused in a certain timeframe. Listening to the shippers’ perspective from a global consumer products company reveals how tricky the problem can become. They subcontract many different LSPs, each using different CO2 calculation methodologies and emission factors, so asking each for the CO2 values would result in numbers that cannot be easily aggregated or compared, therefore they prefer doing the calculations themselves (even though they lack the needed activity data). Without real data, companies revert to global or industry averages to estimate environmental indicators, which leads to average results that do not differentiate alternatives or foster improvement.
The next example area is the environmental compliance of products, which is driven by regulations and affects many industries. Prominent examples of compliance requirements are two EU directives for electronic and electrical equipment, namely RoHS (Restriction of Hazardous Substances) and REACH (Registration, Evaluation, Authorization and Restriction of Chemicals). The former directive limits the use of six hazardous substances, e.g. lead and mercury, to 0.1% by weight of the electric or electronic component and the latter requires reporting any amount of chemical substance used in production or imported to Europe that exceeds 1 ton per substance. To ensure compliance with such regulations, OEMs request from their suppliers data on the materials and substances used in the components they procure. On one hand it’s the OEMs who need to comply with the regulations, and on the other hand it’s the suppliers who own the data and need to provide it for each requesting client. Insights from discussions with OEMs reveal a surprisingly low rate of supplier responses, probably attributed to the significant overhead that doesn’t have an obvious ROI for the suppliers. Again we see the data availability & quality problem recurring due to the inter-organizational nature of sustainability.
Finally, many companies are performing life cycle assessments to determine the environment footprints of some of their key products, and find new way to reduce this, often by modifying some product design decisions. Drivers for product footprinting and sustainable design are a mixture of internal motives (e.g. improving and protecting their brand) and external ones (e.g. customer requests and competitive positioning). The challenge here is also due to the inter-organizational nature of the problem: most of the environmental lifecycle impacts of products are often not generated by the brand-owners, but rather upstream or downstream in the value chain. For example, food brand owners such Unilever and Danone perform bottling and packaging operations that have a relatively low environmental footprint, whereas most emissions were caused by material production and transport (upstream suppliers). Also, high-tech brand owners such as Lexmark and Philips assemble final products, but most environmental impact is due to raw material extraction and the end product’s energy consumption. A study by Unilever shows that only 3% of the greenhouse gas emissions from 1500 representative products of their portfolio are due to their manufacturing, while 94% is due to raw materials and consumer use. This problem requires brand-sensitive companies to engage with suppliers, and the collection of high quality data is the first step towards reducing the environmental impact. According to an LCA expert in an electronics and electrical engineering company, only 5% of their studies actually rely on such primary data and the rest are quick scans using industry averages. With this company already considered a sustainability leader, the severity of the problem in other companies can be extrapolated.
To approach these problems, OEPI envisions a solution that connects participating organizations in a many-to-many network where they can share sustainability indicators, thereby reducing the efforts for provisioning the data and at the same time improving the data availability, quality and reliability. The “many-to-many network” aspect is probably the single-most important underlying concept that can address the problems outlined above. Such networks are very limited in business environments today, despite being very successful in the consumer world (think “Facebook”). Probably the only really successful many-to-many networks in a business context are limited to personal networking applications such as LinkedIn and Xing. However, these are still used by people representing themselves and not their companies. The vision of business applications running on many-to-many networks where companies connect with each other, collaborate, share data, and execute processes is a bold one, but definitely one worth investigating. Being a relatively small research project, OEPI will only start developing this vision into a first prototype, covering focused use cases within environmental sustainability and not the whole exploitable landscape. In the next part of this post, we will investigate, based on the problems outlined above, how such a solution can bring value to the different companies, and thereby justify a business case for a solution provider and the participating users.
OEPI Workshop 13th October 2011
We would like to invite you to a workshop meeting that we are going to
arrange on October, 13th 2011 in Oldenburg. This workshop will introduce
the Oldenburg Environmental Technology Network (www.uno-oldenburg.de) as
a framework for companies to concentrate their forces and knowledge on
common projects that further a sustainable development.
At the same time the recently started project “IT-for-Green: Next
Generation CEMIS for Environmental, Energy and Resource Management”
(www.it-for-green.eu) is presented as a cooperation of 4 universities
and 6 industrial and municipal partners. Within the context of the
ertemis network (www.ertemis.eu), IT-for-Green aims at making companies
and their processes environmentally sensible and – on the long run – at
making cost reduction and environmental protection going hand in hand.
As a complementary topic to OEPI, this project offers the chance for
intensive knowledge exchange.
The workshop is mainly held in German language, but an international
corner with guest from South Africa is also offered.
You will find here an introduction to the ontology for environmental performance indicators. Due to its origin in the OEPI project , the ontology is referred to as the “OEPI Ontology” though it is intended to be useful beyond the scope and duration of this project .
In order to keep this article tight we provide only basic information about the ontology that is helpful to browse, understand, and possibly use or extend it on your own. For a first understanding of OWL ontologies you may read the good tutorial of Matthew Horridge [7]. Details about installation and use of the required tools or interfaces can be retrieved from the references given at the end. Further insight into the domain of environmental performance indicators can be gained through other publications on the OEPI project website [1].
What is the OEPI Ontology about?
Today, there is a great variety of environmental performance indicators (EPI). Their main purposes, for example according to Jasch in [4], are:
- to quantify the current environmental performance of some entity,
- to track the change of environmental performance of an entity between different points in time,
- to compare or to benchmark the environmental performance of different entities,
- to utilize the environmental performance of entities in decisions, or
- to assess the achievement of quantitative goals related to environmental performance in transformation or improvement processes.
The OEPI Ontology defines the concepts that are needed to describe EPI in a formalized, computer-accessible way. The aim is to establish a common understanding of their meaning and interpretation across different organizations and applications.
The ontology establishes the “reference body of domain knowledge” for the implementation of the OEPI platform and the OEPI user portal. The OEPI platform can be regarded as a common resource of selected and preprocessed data for defined EPI from various data sources. The OEPI user portal is the single point of access for the business-oriented user who is neither an expert of the domain of EPI nor an expert of web technology. The portal relies on the OEPI platform to feed its collection of use-case-oriented services with a wealth of suitable EPI data whenever users request it.
The OEPI Ontology uses Web Ontology Language OWL [5] and has been built with the ontology editor Protégé 4 [6]. Though the ontology will evolve further during the remaining course of the OEPI project, a first version is available for the interested public now through the OEPI website.
Some hints to get you going on your own exploration
When you open the OEPI Ontology in Protégé, the ontology annotations are displayed by default. They give you some information related to the current version of the ontology. In the respective functional tabs, you will find views of the asserted hierarchies of classes, properties, and individuals. Further insight can be obtained by running a reasoner (We recommend to use the Hermit reasoner [8] which can be selected from menu “Reasoner“ in Protégé 4.1.) to produce and display the inferred hierarchies.
Furthermore, you will see that all entities of the OEPI Ontology have comments (as annotation properties) that give up-to-date textual descriptions of the meaning and purpose of the entity. They are not replicated here for obvious reasons and will help you to explore the ontology in depth on your own.
For example, figure 1 shows a screen capture of the OEPI Ontology loaded in Protégé 4.1. We have opened the functional tab “Entities”. After running the Hermit reasoner, we have selected class EPI_Statement in the class hierarchy. Therefore, its annotations and its description can be browsed in the respective windows on the right side of the screen. On the left side below the class hierarchy, we could explore the object property hierarchy.
OEPI Ontology in Protégé 4.1, sample view
Some more hints to support your exploration
The OEPI Ontology contains a class hierarchy with primitive as well as defined classes, a considerable number of object properties, data properties, and individuals.
Primitive v. defined classes: The aim might be to have only defined classes in the end but in the current state primitive classes clearly indicate that their definition is considered to be incomplete. There might as well be defined classes in the current version that are still incomplete (with regard to the concept they represent) but have been converted to “defined” for reasoning purposes. They will probably need further revisions in the future.
Use of property characteristics: There has been no intense use of the characteristics of properties so far, except that for object properties inverse properties have usually been created when appropriate. Furthermore, the characteristic “functional” has been used for some object properties rather than introducing a cardinality of 1 in an object restriction.
Naming scheme: Names of classes and individuals (e. g. EPI_Statement) start with a capital letter, property names (e. g. is_Compliant_With) begin with a small letter. Names may be composed of multiple segments separated by the underline character. The first letter of the second and all further segments is a capital for all kinds of entities. Special characters other than the underline character and “white space” have not been used in names.
Roles of individuals: Some of the individuals are considered as part of the ontology (e. g. EPI_Descriptor_Aspect_Emissions) and some have been created only as example data for the purpose of demonstration. The individuals of this second kind have a name prefix “DEMO_” to make them easily recognizable (e. g. DEMO_Product_KONE_Monospace).
Two flavours of class definitions: When looking closely at class definitions, you might encounter two different approaches to class definitions. The first one, which might be called “descriptive”, aims to express the necessary defining conditions for members of the class as complete as possible within the scope of OEPI. The second one, which might be called “inferring”, exploits the reasoning mechanism to derive membership in classes by the existence of a few distinctive object properties (or even only one). There are representatives of both kinds in the OEPI Ontology and furthermore some class definitions which combine both approaches. There might be no general reason for a class to be defined in either of these ways beyond reflecting the current “state of cognition” in OEPI ontology development.
Some classes are defined in a very simple way because they are candidates to be re-used from other existing ontologies in the future, for example class Bibliographical_Information which is currently equivalent to
Thing and has_Simple_Bibliographical_Information some string
Therefore, bibliographical information is currently just represented by a functional data property linking to a character string, which is expected to hold the full bibliographical information.
In the Focus – the “EPI Statement”
In a simplified view, environmental performance indicators are different ways of expressing certain aspects of environmental performance of entities. In the OEPI Ontology, an “EPI Definition” describes all relevant characteristics of a specific EPI. If we use one defined EPI and assess it for a real entity, the result constitutes what is called an “EPI Statement” in the ontology. (See the “OntoGraf” visualization (created by the OntoGraf plugin in Protégé 4.1 which is very useful to visualize the ontology) of this class and its neighborhood). An EPI statement states quantitative information about a specific entity, which is called the “observed” entity, with all additional details that are required to make the statement compliant with its associated EPI definition.
Class EPI_Statement and neighborhood
Different kinds of EPI statements and their typical structure and content are represented in the ontology by different subclasses of EPI statements, for example EPI_Statement_ODP for statements about ozone depletion potential (ODP). An individual member of such a class would be one concrete data record matching this kind of EPI statement.
Conclusion
Hopefully, you are now keen to dig deeper into the OEPI Ontology on your own. This short introduction has provided you with hints where to start and how to do this. Now you may download the ontology – and an ontology editor if you don’t use one already – and jump into the water. Your feedback will be highly welcome at this website.
Download
References
[1] OEPI Consortium, Project website http://www.oepi-project.eu/
[2] OEPI Consortium, Deliverable D1.3: Reference Ontology for EPIs – Requirements and Design, January 2011
[3] OEPI Blog, Why OEPI needs to invest in ontology development, http://oepi-project.eu/blog/2011/05/18/why-oepi-needs-to-invest-in-ontology-development/, May 18, 2011
[4] Christine Jasch, Environmental performance evaluation and indicators, Journal of Cleaner Production, Volume 8, Issue 1, February 2000, Pages 79-88
[5] OWL Working Group, OWL 2 Web Ontology Language Document Overview, W3C Recommendation, 27 October 2009, http://www.w3.org/TR/2009/REC-owl2-overview-20091027/ (accessed: May 30, 2011)
[6] The Protégé Ontology Editor and Knowledge Acquisition System, website http://protege.stanford.edu/ (accessed: May 30, 2011)
[7] Matthew Horridge, A Practical Guide To Building OWL Ontologies Using Protégé 4 and CO-ODE Tools, Edition 1.3, The University of Manchester, 2011, most recent version available for download at http://owl.cs.manchester.ac.uk/tutorials/protegeowltutorial/ (accessed: June 14, 2011)
[8] Hermit OWL Reasoner, website http://www.hermit-reasoner.com/ (accessed: May 30, 2011)
Why OEPI needs to invest in ontology development
Why the development of environmental indicators needs ontologies
Investment in ontology design for the OEPI project is not to be taken literally as “just ontology”. It is one important and necessary part of the Semantic Web approach and thus this investment is actually an important step in setting the direction for the whole implementation approach in OEPI. Therefore, the actual question is why OEPI needs to go for application of Semantic Web technologies with Ontology being a part thereof.
It is about this alternative:
Is OEPI technology old-fashioned and obsolete before its implementation even has begun and fails to meet its goals? – Or is OEPI technology future-oriented and able to leverage the capabilities of “the Web” even beyond its current exploitation and succeeds in fulfilling its mission?
The mission of OEPI is to deliver more than what is possible today regarding Environmental Performance Indicators and their widespread use.
Semantic Web technologies – including ontology as one important piece – are necessary to ensure the fulfilment of this mission, in more detail:
- OEPI wants to use and to combine as much existing (web) resources as possible and bring them close to the user. The ability to exchange the semantics of such resources is of paramount importance for the practical implementation.
- OEPI wants to automate these processes in an easy and user-friendly way.
- OEPI wants to enable flexible and ad-hoc use of resources.
Conventional technologies alone, even including modern concepts as, for example, Model Driven Architecture or Service Oriented Architecture, are helpful but not sufficient to meet these requirements because they lack explicit semantics of the modelled domain as also of existing data and services. Currently, conventional Systems and Software Engineering is able to solve configuration tasks in advance and according to predefined decision patterns but not
by inferring decisions from semantically rich descriptions, constraints, and restrictions at runtime. Semantic Web technologies add capabilities for provision of such features to the conventional technologies. One first approach to bridge the gap from ontology to conventional MDA, which is quite common already, could be the definition of a domain-specific UML profile
representing ontology.
OEPI needs Semantic Web technologies (in combination with conventional technologies) to describe, to categorize, to find and to utilize existing resources and to formalize spontaneous requirements of users for supporting their daily work flexibly.
In this approach, the ontology has the purpose to unambiguously and explicitly describe all the “OEPI things” with their properties, relationships, and semantics in a formalized way that can be evaluated by humans and by software. It constitutes the common reference and resource for all data models, service descriptions etc. that have to be developed for the implementation of the platform as well as for different independent services or solutions. It is
needed to enable and ensure semantic interoperability among existing, new, and future services and solutions.
OEPI will not be able to fulfil all these requirements completely during the limited course of the project but it is responsibility of OEPI to provide a sound foundation that can be built on and further extended and enhanced by industry as well as research beyond the end of the
project. OEPI Ontology will be one part of this foundation.
Reference:
“Ontology Driven Architectures and Potential Uses of the Semantic Web in Systems and Software Engineering”, Working Group Note of W3C Semantic Web Best Practices & Deployment Working Group, http://www.w3.org/2001/sw/BestPractices/SE/ODA/
Top Level Ontologies for OEPI
An overview of top level ontologies
Ontologies are mostly created for a specific purpose as for OEPI that is the creation of an ontology for environmental performance indicators. Apart from these specific ontologies there are also more general ones that are called top level or upper ontologies. They serve as a kind of superstructure above the specific domain, task and application ontologies. Its purpose is to provide the possibility to integrate specialized ontologies into a generic semantic context that is levelled above them. Examples for such ontologies are those that provide proper integration of concepts into the earth system, common time and space knowledge or just proper mapping to a multilingual dictionary or thesaurus.
During the development of OEPI’s ontology for environmental performance indicators, several top level ontologies have been reviewed. This is part of the ontology design process as by that it may be avoided to create an own comprehensive top level ontology which is a complex and lengthy task. The basis for the research done was a comprehensive review done by Mascardi, V., Cordì, V. and Rosso, P. (2007): A comparison of upper ontologies, Proceedings of Conf. on Agenti e industria: Applicazioni tecnologiche degli agenti software, WOA07, Genova, Italy, pp. 24-25. They developed a set of criteria to evaluate several ontologies. By that we could have a better understanding of the most important top level ontologies that exist.
The evaluated ontologies are in detail:
- The Basic Formal Ontology (BFO)
- The Cyc Project
- DOLCE
- The General Formal Ontology (GFO)
- The PROTo ONtology (PROTON)
- Sowa’s ontology
- The Suggested Upper Merged Ontology (SUMO)
During our review we found that most top level ontologies have common agreements of top level issues like objects, world, properties, events or processes. Nevertheless many of the evaluated ontologies are already geared toward specific purposes like for example the medical domain, natural sciences or language processing. So it will make more sense to have the OEPI ontology mapped to existing abstract concepts of one or several of the ontologies from above.
If you know of additional top level ontologies that may be added to this list, please let us know and we might include it here.
Related projects to OEPI
Related eu projects to OEPI
Different efforts are now emerging towards the creation of infrastructures and platforms for Environmental Information Systems and Services – including Infrastructures for flexible discovery and chaining of distributed environmental services.
Information and Communication Technologies (ICT) have an essential role to play in the context of Environmental systems as they provide the necessary support in terms of tools, systems and protocols to establish a dynamic environmental space of collaboration in a more and more sophisticated digital world.
It is interesting how many interesting projects related to OEPI have shown up on the landscape as of today. I hope you enjoy the overview:
- ENVISION (website)
ENVIronmental Services Infrastructure with Ontologies - NETMAR (website)
Open service network for marine environmental data - OEPI (website)
Exploring and Monitoring Any Organisation’s Environmental Performance Indicators - PESCADO (website)
Personalized Environmental Service Configuration and Delivery Orchestration - SUDPLAN (website)
Sustainable Urban Development Planner for Climate Change Adaptation - TATOO (website)
Tagging Tool based on a Semantic Discovery Framework - UncertWeb (website)
The Uncertainty Enabled Model Web - UrbanFlood (website)
Building an Early Warning System Framework for European Cities - GENESIS (website)
GENeric European Sustainable Information Space for environment - ICT-ENSURE (website)
ICT for Environmental Sustainability Research - GIGAS (website)
GEOSS INSPIRE and GMES an Action in Support - Sany (website)Sensors Anywhere
- Orchestra (website)
Open Architecture and Spacial Data
If you know of more projects we have forgotten drop us a comment.
TP
About the OEPI Project
How environmental indicators can help business sustainability
The OEPI project will provide a platform which enables its users to determine the environmental performance indicators for products and services they are offering. To this purpose, the different data sources of relevant organizations or service providers that provide the corresponding values for materials and processes, will be integrated into the platform.
OEPI will even provide for indicators that are defined differently, a conversion facility presenting such indicators in a way which makes them directly comparable for the users. Users are provided with a variety of analysis tools and output channels. Thus, they can easily generate corresponding performance reports. Here, standardization is crucial, since the complete value chain with all its suppliers shall be covered in the sustainability balance enabled by OEPI including – possibly certified – documentation.
Find out more under http://www.oepi-project.eu
Welcome to the OEPI Blog.
The OEPI Blog will keep you up to date with current news, events and related material on organizational economical performance indicators.
Please also check out the project-website: http://www.oepi-project.eu