TEXBIAG

Context

Bioenergy from agriculture is today in the heart of sustainable development, integrating its key components: environment and climate change, energy economics and energy supply, agriculture, rural and social development. Fighting against climate change imposes the mitigation of greenhouse gases. Considerable efforts have to be pursued, especially in the field of energy production and use. Recent energy crises have reminded our policy and economic decision makers of the importance of energy, of a secure and diversified energy supply in our economies. Agriculture in Europe is at a turning point, leading to important questions about the diversification of agricultural productions and sources of incomes for farmers, the use of rural and arable lands for food and non-food crops, the contribution of agriculture to climate change fighting and renewable energy supply.

Objectives

The final objective of the project is to lead to an actual and significant contribution of bioenergy from agriculture to the mitigation of greenhouse gases emission, to a secure and diversified energy supply and to farmers’ incomes and rural development.To reach this final objective, the TEXBIAG project will develop three specific tools: A database of primary quantitative data related to environmental and socio-economic impacts of bioenergy from agriculture integrating biomass logistics; A mathematical model “monetizing” bioenergy externalities from agriculture; A prediction tool assessing the impacts of political decisions made in the framework of the development of bioenergy from agriculture on different economic sectors (energy, agriculture, industry, and environment).

Description of tasks

Applying the principles of the systemic methodology, the project implementation is structured as follows: Task 1. Database construction: •Conception of the database, in collaboration with the partners in charge of the development of the decision-making tools; •Data and model collection from literature and measurements for missing data and filling the database with collected information and operation; •Survey and analysis of existing studies carried out on logistics of biomass supply chain from agriculture; •Feed-back from the decision-making tools and adaptation/updating of the database. Task 2. Externalities monetary value model: •Contribution to database construction through a continuously improved model; •Analysis of existing studies and models, comparison and evaluation; •Building of a qualitative model to put in evidence causal relationships (detection of induced effects); •Costs / revenues analysis in order to reach monetary valuation; •Building of a quantitative externalities monetary value model. Task 3. Policy prediction tool, based on an existing model: •Addition to the existing tool of new targets, such as job creation (direct and indirect employment), rural development, energy supply security, added value, and other externalities; •Addition of technology routes not yet considered in the previous model (DME, hydrogen, biogas, biorefineries, etc.); •Addition of missing commodities such as water and other relevant externalities; •Modelling of non-linear perturbations effects: electricity system, refineries, secondary products such as animals feeds, agro market perturbation, etc; •Addition of the externalities monetary value model; •Addition of potential policy measures in the existing model (quotas, subsidies, other measures,..). Task 4. Dissemination and valorisation of the results of the project: •Making a user friendly interface to use the software tool (data access & update, policy measures, sensitivity analysis); •Dissemination of the results through communications tools (brochures, posters, website, conferences, workshops, etc). CRA-W is task leader for Tasks 1 and 4 and is coordinator of the project. VUB is task leader of Task 3. FUNDP is task leader of Task 2. KUL assists VUB in the execution of Task 3. VUB, FUNDP, KUL assist CRA-W in the execution of Task 4.

Expected results

The long-term impacts of the project are expected to be: 1.An increase of the level of awareness among policy makers regarding policy gaps and policy implementation issues in Belgium regarding bioenergy from agriculture; 2.The implementation of policy reinforcement and policy implementation guidelines in renewable energy; 3. Stimulation of rural development by creating employment opportunities in relation to the implementation of bioenergy projects from agriculture; 4.An improvement of the local environment and living conditions through the introduction of modern and efficient bioenergy technologies; 5. An improvement of the global and local environment through the introduction of modern and efficient bioenergy technologies by reducing the air emissions associated to fossil fuels combustion hereby reducing the amount of Greenhouse Gases (CH4 and CO2) emissions.

Results obtained

First results:

Bioenergy chains to be studied in priority by the project have been selected according to stakeholders' opinion on their relevance for the Belgian market.

Data related to environmental and socio-economic impacts of biomass and bioenergy are being compiled and validated into the Belgian context through expert consultation. It is indeed essential to use appropriate data since these vary a lot according to local conditions (climate conditions, agricultural practices). Data will then feed the two models: the externalities monetary value model and the policy prediction tool.

Bioenergy externalities have been selected. Relevant externalities for biomass and bioenergy can be classified in 3 categories: (1) environmental externalities, such as GHG emissions, carbon stocks, environment quality, etc.; (2) socio-economic externalities, such as food security, workers' rights, land property rights, etc.; and (3) macro-level issues, mainly related to indirect land-use changes, which can have disastrous consequences in terms of GHG emissions, biodiversity losses and socio-economic impacts, etc.

Bioenergy externalities need to be assessed either quantitatively and/or qualitatively in order to be introduced into the monetary value model and the policy prediction tool. Qualitative and quantitative indicators based on selected externalities are being developed and refined according to expert consultation and scientific releases. Wherever possible, monetization of these externalities is proposed. In case no monetization is possible a qualitative assessment using a "traffic light" code is used. This aims at drawing attention on potential risks for a given externality. Monetized indicators will be introduced in the policy prediction tool (System Perturbation Analysis or SPA) in order to enhance policy makers’ choice of the best bioenergy routes. Additionally monetized and non-monetized indicators will be displayed in tables with all monetized, quantitative and qualitative information on each bioenergy route selected (one table per bioenergy route). These tables will allow policy makers to take into account all dimensions of sustainable development in their choice of the best bioenergy routes to support.

Since bioenergy externalities are not stand-alone impacts, selected externalities are being articulated into a qualitative model in order to identify cause-effect relationships, feedback, induced and non-linear effects between them. Systems dynamics and indicators are being used to describe and assess these potential links. The qualitative model defines links between externalities, studied separately, and characterizes these relations into positive (correlation), negative (inverse) or indeterminate. From this modelling, it appears that many interactions between bioenergy externalities are not straightforward. Many of them are time or space dependent. Agricultural practices vary a lot from one region to another; indirect effects are far from being understood and assessed correctly, long-term effects of climate change are still unknown, etc. The qualitative model is iteratively refined through interactions with experts in workshops and brainstorming sessions. Since a lot of research efforts are still ongoing on many of these parameters (climate change, biodiversity, indirect effects, etc.) it is also important to keep an eye on scientific releases in order to improve this model.

The policy prediction tool is based on existing software called System Perturbation Analysis (SPA). The method consists in perturbing a resource (e.g. replacing a food crop by non-food crop on a single hectare) and analysing all direct and indirect impacts on a given system (Belgium). The tool is further developed to compute impacts other than energy and CO2eq, namely other effects on environment, employment, added value and others. In order to quantify the externalities, one single resource chosen by the user is perturbed with a specified amount. This automatically leads to a perturbation of at least one main product and in general also of several by-products. Since the amounts of products are considered to be constant, the perturbations on the products must therefore be compensated by perturbations on at least one other resource, which on his turn may induce other perturbations in the products, etc. The SPA software is being improved regarding some of weak points. In a first version of the software (SPA1), all the effects of perturbations on the system were considered as linear, leading to oversimplification for certain types of perturbations. Mathematical models for the Belgian animal feed market, refining industry and food market in Belgium are developed in order to improve the effect of perturbations in the SPA software.