(692c) Multi-Objective Optimization Combined with Input-Output and Eco-Cost Assessment for Decarbonizing the European Economy

Multi-objective optimization combined with input-output and eco-cost assessment for decarbonizing the European Economy

D. Cortés-Borda ^a, L. Jimenez-Esteller ^a, Guillén-Gosálbez ^a,b,

a.Departament d’Enginyeria Química, Universitat Rovira i Virgili, Av. Països Catalans, 26, Tarragona E-43007, Spain.

b.Centre for Process Integration, School of Chemical Engineering and Analytical Science, The University of Manchester, Manchester M13 9PL, UK

The world is presently facing environmental and economic challenges derived from the excessive consumption of non-renewable sources. The accelerated increase of fossil-derived greenhouse gas (GHG) emissions has brought global warming to alarming levels. As a result, worldwide governments have set up international agreements in which the participants are committed to reduce their GHG quota by increasing the share of renewable energy sources in their energy mixes (among other alternatives).

So far, the global share of renewable energies (including wind, geothermal, solar, biomass and waste energies) represents only 1.8% of the world’s energy consumption [1]. Further increases in the global renewable energy share is challenging due to the low competitiveness of such technologies. Hence, GHG saving strategies based on the deployment of renewable energies might result unsuccessful without environmental policies that place the renewable technologies in the same economic threshold than fossil fuels.

Since the late 90’s, governments of the most developed countries have included taxation mechanisms in their policy agendas to overcome the competitiveness barriers that environmentally conscious alternatives face [2]. Defining environmental taxes on GHG intensive products re-directs the economy towards GHG saving alternatives. That is, the end users would prefer less GHG-intensive products (after an increase of GHG taxes); and consequently the producers would need to improve their environmental efficiency in order to remain competitive. Furthermore, environmental taxation leads to a “double dividend”, since revenues from taxes could be re-invested to compensate the environmental damage [3] or to prevent future emissions by investing in cleaner production technologies [4-5]. Nowadays, the “eco-cost” is well known as the cost of preventing a given environmental burden [6]. Numerous studies have assessed the eco-cost in different industries/sectors [8-16], however, so far there are no studies integrating the eco-cost assessment of industries as a whole economy.

The environmentally extended input-output models (EEIO) [17] provide a comprehensive description of the inter-industry economic transactions, which allows linking the total economy (as a whole) with its corresponding environmental burdens and associated eco-costs. Furthermore, the flexibility of EEIO models allows allocating the environmental responsibilities both to consumers or producers, depending on whether the assessment is consumption-based or production-based.

Combining EEIO with multi-objective optimization algorithms is an appealing methodology to identify solutions that are economically and environmentally optimal in an economy. On this basis, the aim of this work is to minimize the eco-costs derived from the GHG emissions of the European Economy; while simultaneously maximizing the demand satisfaction of the final customers.

To this end, we formulate the EEIO model as a multi-objective optimization linear programming (LP) problem with two objective functions (the eco-cost of greening the European economy; and the total demand satisfaction); subject to a system of linear equations representing the macroeconomic transactions in an economy; and the environmental extension equations. The LP model leaves the final demand of each sector of the European Economy as a free variable that can range within realistic upper and lower bounds, in order to find the optimal solutions. To investigate the existence of tradeoffs between the two competitive objectives, we apply the epsilon-constraint method that generates a set of Pareto optimal solutions [18]; each of which presents a unique combination of demand satisfaction and eco-cost savings.

We applied our method to the 25-country European Union’s Economy (EU-25) (The European Union according to the 2004 enlargement). The macroeconomic and environmental data was retrieved from the E3IOT database [19-20], which contains highly disaggregated input-output tables (i.e. 487 industrial sectors and household activities).

A preliminary analysis was first performed in order to compare the eco-cost of each sector with its corresponding total economic output. Hence, by comparing each sector’s direct and indirect associated eco-costs (production-based and consumption-based, respectively) with their own economic output, we find that there are 16 sectors (from the production perspective) and 13 sectors (from the consumption perspective) that generate more eco-costs than the economic output they generate. Among these sectors, we find the electric service (utilities), petroleum refining, eating and drinking places, meat animals, poultry slaughtering and processing, fluid milk, to name some.

The optimization results provide insight into which sectors should be regulated first in order to reduce the GHG emissions and the associated eco-cost. Hence, the European Commission should firstly regulate the sectors identified by our model (by either investing in improving the efficiency or establishing stronger environmental regulations on their final demand) in order to move towards a more sustainable economy.

References

[1]       N. Lior, “Sustainable energy development: The present (2009) situation and possible paths to the future,” Energy, vol. 35, no. 10, pp. 3976–3994, Oct. 2010.

[2]       P. Ekins, “European environmental taxes and charges: recent experience, issues and trends,” Ecol. Econ., vol. 31, no. 1, pp. 39–62, Oct. 1999.

[3]       C. N. Eng-ct, R. Dones, T. Heck, C. Bauer, S. Hirschberg, P. Scherrer, I. Psi, P. Bickel, P. Preiss, U. Stuttgart, L. I. Panis, and I. De Vlieger, “ExternE-Pol Externalities of Energy : Extension of Accounting Framework and Policy Applications New energy technologies,” no. x, pp. 1–76, 2005.

[4]       J. G. Vogtlander and A. Bijma, “The ’Virtual Pollution Prevention Costs ‘99’: A single LCA-based indicator for emissions,” Int. J. Life Cycle Assess., vol. 5, no. 2, pp. 113–124, 2000.

[5]       J. G. Vogtländer, A. Bijma, and H. C. Brezet, “Communicating the eco-efficiency of products and services by means of the eco-costs/value model,” J. Clean. Prod., vol. 10, no. 1, pp. 57–67, Feb. 2002.

[6]       J. G. D. U. of T. Vogtländer, “Ecocost value.” [Online]. Available: www.ecocostsvalue.com.

[7]       J. G. Vogtländer, E. Lindeijer, J.-P. M. Witte, and C. Hendriks, “Characterizing the change of land-use based on flora: application for EIA and LCA,” J. Clean. Prod., vol. 12, no. 1, pp. 47–57, Feb. 2004.

 [8]      J. Vogtländer, P. van der Lugt, and H. Brezet, “The sustainability of bamboo products for local and Western European applications. LCAs and land-use,” J. Clean. Prod., vol. 18, no. 13, pp. 1260–1269, Sep. 2010.

 [9]      M. A. Morales-Mora, E. Rosa-Dominguez, N. Suppen-Reynaga, and S. A. Martinez-Delgadillo, “Environmental and eco-costs life cycle assessment of an acrylonitrile process by capacity enlargement in Mexico,” Process Saf. Environ. Prot., vol. 90, no. 1, pp. 27–37, Jan. 2012.

[10]    L. Čuček, R. Drobe?, B. Pahor, and Z. Kravanja, “Sustainable synthesis of biogas processes using a novel concept of eco-profit,” Comput. Chem. Eng., vol. 42, pp. 87–100, Jul. 2012.

[11]    E. Igos, E. Benetto, I. Baudin, L. Tiruta-Barna, Y. Mery, and D. Arbault, “Cost-performance indicator for comparative environmental assessment of water treatment plants.,” Sci. Total Environ., vol. 443, pp. 367–74, Jan. 2013.

[12]    A. Mestre and J. Vogtlander, “Eco-efficient value creation of cork products: an LCA-based method for design intervention,” J. Clean. Prod., vol. 57, pp. 101–114, Oct. 2013.

[13]    M. A. M. Mora, E. R. Dominguez, A. A. Ibarra, N. S. Reynaga, and S. A. M. Delgadillo, “A methodological improvement for assessing petrochemical projects through life cycle assessment and eco-costs,” Int. J. Life Cycle Assess., vol. 19, no. 3, pp. 517–531, Oct. 2013.

[14]    N. M. van der Velden, M. K. Patel, and J. G. Vogtländer, “LCA benchmarking study on textiles made of cotton, polyester, nylon, acryl, or elastane,” Int. J. Life Cycle Assess., vol. 19, no. 2, pp. 331–356, Sep. 2013.

[15]    R. Rivas-Hermann, J. Köhler, and A. E. Scheepens, “Innovation in product and services in the shipping retrofit industry: a case study of ballast water treatment systems,” J. Clean. Prod., Jun. 2014.

[16]    S. Ferreira, M. Cabral, N. F. da Cruz, and R. C. Marques, “Economic and environmental impacts of the recycling system in Portugal,” J. Clean. Prod., vol. 79, pp. 219–230, Sep. 2014.

[17]    W. Leontief, “Environmental repercussions and the economic structure : an input-output approach” vol. 52, no. 3, pp. 262–271, 1970.

[18] Ehrgott, M. Multicriteria Optimization, second ed. Springer, Berlin. 2005.

[19]    G. Huppes, A. de Koning, S. Suh, R. Heijungs, L. van Oers, P. Nielsen and J.B. Guinée. Environmental impacts of consumption in the european union: High-resolution input-output tables with detailed environmental extensions. Journal of Industrial Ecology 10(3): 129-146. 2006

[20]    R. Heijungs, A. de Koning, S. Suh and G. Huppes. Toward an information tool for integrated product policy: Requirements for data and computation. Journal of Industrial Ecology 10(3): 147-158. 2006.