European Sustainable Chemicals Support Service: Promoting Sustainability in Europe's Chemical Industry

Introduction

The European Sustainable Chemicals Support Service is a project financed by the European Commission's Directorate General for Growth. ¹ Its main aim is to support European regions in developing a sustainable chemical industry based on three types of raw materials: biomass, waste, and effluent gases (such as carbon dioxide (CO₂). The European Commission awarded the tender to conduct this project to a consortium led by Spanish Research Centre for Energy Resources and Consumptions (CIRCE), and includes the consultancy firm PNO Consultants and the European Chemical Industry Council (CEFIC). Work on the project began in February 2016 and will go on for eighteen months, divided into two parts.

The first part consists in developing a self-assessment tool based on an online, automated, and modular analysis, and will be the basis for European regions to improve and adapt their regional strategies in order to boost sustainable economy in their area. The self-assessment tool will be freely and publicly available to any European area interested in using it on the European Commission web page.

The second part of the project consists in lending technical support and providing consultancy services to six European regions, called Model Demonstration Regions (MDRs), in order to strengthen their strategies for drawing in the investments they need to produce sustainable chemicals. ²

This article describes the project aims as well as the activities to be carried out with the six MDRs, including some first conclusions based on the ongoing analysis and works with those regions.

Self-Assessment Tool

The self-assessment tool (SAT) focuses on the following three main feedstock categories for sustainable chemicals:

• Biomass from agriculture and forestry; mainly to be processed in high capital and energy intensive installations (e.g., integrated biorefineries).

Working Method

Within a specific feedstock category, a set of accurate, self-explanatory, and straightforward questions for each KF has been defined according to a three-step approach. First, start from the definition of an ideal case/scenario for each feedstock category and KF. An in-depth analysis of the role, needs, and barriers found for the alternative feedstock(s) considered is a core activity in the questionnaire-building process, ultimately paving the way towards a Sustainable Chemical Industry. Best practices found worldwide will help to draw the scenario.

Secondly, 5–10 main features that comprise a representative description of the scenario and make it ideal are identified.

Thirdly, 5–10 questions related to the main features described in step two are established in order to obtain the relevant information that allows for a thorough characterization of the particular region's suitability given the KFs. More than one question may be needed for the description of a single main feature, but no more than 10 questions must be set for each KF. In the end, appropriate and straightforward questions, which that avoid excessive technicality technical as well as room for misunderstanding, are formulated. Having a limited number of questions—5 to 10 per KF—ensures the whole questionnaire remains of reasonable length. This prevents the respondent from taking too long to complete it, but ensures the necessary information is collected to provide the regions with useful and specific conclusions and recommendations.

There are mainly two types of questions in all three questionnaires: multiple-answer questions and single-answer questions. No open questions have been included in the questionnaires (further discussion on the type and weighting of the answers is included below).

Moreover, cascade questions—in which a certain answer prompts additional questions to narrow down the information needed to accurately describe the current situation of the region—are employed. Some KF questions in cascade may appear among other KFs.

Each KF is evaluated on a scale from 0 to 10, taking into account the answers given by the respondent to the questions proposed. The higher the final score in a KF, the closer the region is to the ideal scenario.

Each of the questions displayed per KF is linked to a feature of the ideal case/scenario, according to the three-step approach previously described. However, there may be one or more questions linked to a specific feature. The weighting factor for the questions has been established according to their relevance in the transition towards a strong Sustainable Chemical Industry. The sum of the weighting factor (w_i) of all the questions is always 10, so that all the KF in the different feedstock categories can be compared and graphically displayed.

Lastly, a set of up to 5 answers has been defined for each question, providing the actual and accurate meaning behind each answer—not just a figure from 0 to 1. The set of answers define precisely, but not extensively, the range of situations that can be found in each of the features to be analyzed and described. This includes both the closest to the ideal situation and the worst possible situation. The respondent should easily identify the position of its region with respect to the answers given.

Neutral answers have been avoided so that the respondent tips the balance in one or another direction. Thus, a more in-depth analysis is required from the respondent and more accurate results will be reached.

Potential inconsistency in the answers given by the respondent and conclusions/recommendations given by the SAT can occur, and accordingly a detection module has been implemented in the tool as an Inconsistency Module to avoid incongruous reports. The Inconsistency Module displays a contradiction warning and additional questions, asking for clarification. As a last resort, it hides some contradictory conclusions/recommendations in the final report.

Weighing Mechanisms for Answers

The score linked to each question's answer has been thoroughly established, from 0 to 1, in order to keep the relevancy of the answer as reasonable as possible.

The highest the score of an answer is linked to the closest to the ideal scenario. A score of 1 represents the ideal situation for that specific KF—no action or recommendation in principle will be needed, just best practices—while 0 is the worst scenario possible.

Eventual relations within answers from different questions within the same KF or other KF have been also studied when establishing this weighting mechanism. The robustness of the weighting mechanism has been checked and improved through a sensitivity analysis and validated by the industrial platforms and the MDRs.

The score for that question with a specific answer would be:

• If a₁ is chosen → q₁ = 1 × 3 = 3

That is to say, the result q_n of a question n with an answer a _m : \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*}{{ \rm{q}}_{ \rm{n}}} = { \rm{ }}{{ \rm{S}}_{ \rm{m}}} \times {{ \rm{w}}_{{ \rm{n \;}}}} \end{align*} \end{document}

where s_m is the score of the answer chosen and w_n is the weight factor for the question.

On the other hand, the result q_n of a multiple-choice question would be: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*}{{ \rm{q}}_{ \rm{n}}} = \sum \nolimits_{i = 1}^r {{S_{m \;}}} \times {w_n} \end{align*} \end{document}

where s_m is the score of the answer chosen and w_n is the weight factor for the question.

For this case, when choosing a₁ and a₂, the q₃ would be: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*}{{ \rm{q}}_3} = { \rm{ }}2 \; \times \; \left( {0.2{ \rm{ }} + { \rm{ }}0.2} \right) { \rm{ }} = { \rm{ }}0.8 \end{align*} \end{document}

Additionally, if the multiple-choice answer includes subanswers, the weighting s_m would be multiplied times the score of the subanswer selected as follows: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*}{{ \rm{q}}_{ \rm{n}}} = \sum \nolimits_{i = 1}^r { \; ( {s_m} \times {s_r} ){*} {\rm{ }}{{ \rm{w}}_{ \rm{n}}}} \end{align*} \end{document}

where S_m is the score of the answer chosen, W_n is the weight factor for the question and S_r 0 ≤ s_r ≤ 1, is the score of the subanswer selected within the selected multiple choice answer.

In the case of single-answer subquestions, when choosing an answer then the final score of the question would be: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*}{{ \rm{q}}_{ \rm{n}}} = { \rm{ }}{{ \rm{S}}_{{ \rm{m}}}} * \;{{ \rm{S}}_{ \rm{r}}}{} * \;{{ \rm{w}}_{ \rm{n}}} \end{align*} \end{document}

where S_m is the score of the answer chosen in the question, s_r the score of the answer chosen in the subquestion and W_n the weighting of the question.

In this case, when choosing a₁ in the question and the additional answer a₂ in the subquestion the q₁ will be: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*}{{ \rm{q}}_1} = { \rm{ }}1 \; \times \;0.8{ \rm{ }} = { \rm{ }}0.8 \end{align*} \end{document}

The final result of the KF, always between 0 and 10, will be represented in the spider graph and is the sum of all questions' results: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*}{ \rm{KF}} = \sum \nolimits_{i = 1}^n {{q_n}} \end{align*} \end{document}

Final SAT results

The information gathered from the questions for each feedstock will permit to assess the Investment Readiness Level (IRL) of the region. The assessment results will be given through a final report for each feedstock (biomass, waste, and CO₂). It will be displayed online or through a downloadable .pdf once each of the feedstock questionnaires selected have been filled in.

The SAT is addressed mainly to the region's decision-makers, as it will conclude with general recommendations to be adopted at regional level. Most of these recommendations are related to strategic actions that foster the region's Sustainable Chemical sector.

For each feedstock category, the report will include a representation of a spider chart with the quantification of the IRL for each of the KF assessed, accompanied by the automated and modular interpretation of these results.

The spider chart is a valuable tool to give a first picture of the region in comparison with the European Union average for each KF. Visually, the assessment can detect which areas should be improved in order to develop an effective and successful Sustainable Chemical industry. It will give the information about the main KFs where region needs to improve to obtain its highest IRL. The results will display examples of Sustainable Chemical regions where the weakest KFs of the assessed region have been addressed successfully.

The interpretation of the results includes a general diagnostic of the region's current situation and recommends holistic actions it could take to improve its current IRL. Extension of the recommendations will be no longer than a few paragraphs per each KF. Most of the conclusions and recommendations will be common for KFs 3, 4, 5, and 7, which deal with horizontal issues for the three feedstocks; the rest of KFs results will be more specifically for each feedstock.

The information gathered from the different respondent regions during the SAT life will be stored and used in order to establish and update an EU average and allow benchmarking. At a first stage, the tool will be fed data and results from the Model Demonstration Regions. This average data will be refined and updated over time with the input from other regions.

Assessment of the MDRs

The second pillar of the project is to offer technical assistance and consultancy services to the six MDRs to strength their regional strategies and increase their IRLs for sustainable chemistry production. In particular, investments in biorefineries, water treatment, and recycling and recovery plants for plastics and other substances, together with their associated infrastructures and resource assessments, will be evaluated. A Strength Weaknesses Opportunities Threats (SWOT) analysis is encouraged, as it will consider the specificities of the region characterized by the 10 KF factors. Both the characterization and the results of the analysis have been developed in coordination with the regional actors involved in order to promote a direct increase in the region's IRL. It is recommended that the results be presented in the form of Master Plans where concrete investment possibilities are identified and assessed. In this way, methods for leveraging funding while evaluating the necessary support through public investment in infrastructures, can be determined.

The consultancy services provided follow methodologies as standardized as possible to ensure the homogeneity, comparability, and replicability of the results process. The service provided is composed of three main elements: a report on the region's IRL, peer review meetings, and policy briefings.

Peer Review Meetings

The general objective of peer review meetings is to provide a direct contact with the representatives of the studied region. A workshop is arranged to gather and document comprehensive feedback for the preparation of useful policy briefings and to promote discussion among stakeholders. The peer review meetings have been undertaken following these specific objectives:

• To involve a group of between 10 and 15 region representatives for the region, including the views of the main actors (e.g., industries [3–5], investors [2–4], technology and research community [1–2], regional authority [2–3] and other possible stakeholders of the region).

Policy Briefing

Conclusions

The SAT is going to officially launch in April 2017, and all public authorities are encouraged to use it in the development of their respective regional strategies. Cluster managers will also find it very useful in motivating their members towards the development of more sustainable chemicals.

On the other hand, the first conclusions of the service provided to the Model Demonstration Regions are that it was noted in some cases that all of the investment plans had a positive business case without the use of subsidies. The amount of subsidy was a large factor in deciding the location to execute the plans. Other decisive factors are resource availability and value chain integration. However, the biggest barriers for the investment plan in the regions are all closely related. The market is indicated as the biggest hurdle, closely follow by available financing. The lack of basic biobased chemical feedstock makes every investment more costly and without a proven market financers are acting risk-adverse and will not provide the amounts needed to fund the projects. The narrow R&D focus of subsidies makes it in practice troublesome to acquire grants for implementing biobased processes. Furthermore public policy intensifies the risk of projects since current standards are more based on energy reduction instead of CO₂ elimination from the value chain. Public policy could ease the biobased markets and increase demand, however there are currently no public or law incentives to do so.

In other cases, there is a lack of end-user involvement in basically all biobased innovation projects by the respective regional companies. This can be explained by the significant presence of feedstock suppliers, but a lack of chemical companies which are really interested in biobased products. In some regions, chemical industry is traditionally very fossil feedstock oriented and little breakthroughs have been achieved so far in supplying these industries with biobased intermediates for further processing. Engagement with the chemical industry at regional, national and EU-level is needed to further involve end-users.