**4. Conclusions and Recommendations**

The aforementioned 4th scenario represents the largest proposed area for cardoon, miscanthus and paulownia cultures: 72,313 ha (0.81% of the total area of mainland Portugal), identified in the Regions of Estremadura and Ribatejo, Lisbon, Algarve and part of Beira Litoral and Alentejo, 874 ha (0.01% of the total area of the continent) being located in the Beira Litoral and Alentejo Regions and 80,975 ha (0.91% of the mainland Portugal total area), identified in the Beira Litoral, Estremadura and Ribatejo, Lisbon, Alentejo, Algarve and part of Trás-os-Montes and Alto Douro and Beira Interior, respectively.

Concerning microalgae cultures, the 1st and 4th scenario represent the largest proposed area for its implementation with approximately 29,395 ha in both cases (0.33% of the total area of mainland Portugal) identified in the Beira Litoral, Estremadura and Ribatejo Regions, Lisbon and Set úbal, Algarve and part of Beira Interior.

Concerning cultivated species, the most significant value of the agricultural-forestry area is 6,531,613 ha (73.38% of the total continent) from COS 2015, identified in all regions, anticipating a huge potential for waste generation and recovery.

The previously identified areas for energy crops production correspond only to marginal and degraded soils. The scenarios created yielded very restricted areas which fulfill all predefined parameters for each species.

The GIS is a powerful tool for predicting areas for biomass production to feed energy-based biorefineries and geographical availability of the feedstock. It is an instrument for technicians, beneficiaries and decision-makers regarding the optimal location of future biomass power plants.

The implementation of energy crops in degraded and contaminated soils presents also a dual purpose: it allows the sustainable production of energy and soils can also be recovered for agriculture or forestry. This study combines GIS and a multiplicity of data in order to predict the availability of biomass for bioenergy, acting as support guidelines for further implementation elsewhere, as the methodology can be implemented in other countries or regions.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/1996-1073/13/4/937/s1: S.1 Maps obtained from Direção Geral do Território (DGT); S.1.1 Districts of the O fficial Administrative Charter of Portugal-CAOP; S.1.2 Land Use and Land Cover; S.1.3 Corine Land Cover; Figure S1. Maps obtained from the DGT website: (a) Districts of mainland Portugal from CAOP; (b) COS 2010; (c) CLC 2012; (d) COS 2015; S.2 Maps consulted from Agência Portuguesa do Ambiente (APA); S.2.1 Insolation; S.2.2 Temperature; S.2.3 Precipitation; S.2.4 Frost; Figure S2. Maps presenting APA data (annual basis): (a) Insolation level measured in number of hours; (b) Average daily air temperature in ◦C; (c) Precipitation measured as the total amount (average values) in mm; (d) Map of frost in number of days; S.2.5 Municipalities of mainland Portugal with CO2 production in the energy and industrial sectors; S.2.6 Pollutant Release and Transfer Register—PRTR; Figure S3. Consulted maps from APA (annual basis): (a) Municipalities vs the quantity produced CO2 (emitted and released) in the energy sector; (b) Produced (emitted) CO2 (kt) in the industrial sector vs Municipalities; (c) Location of the companies by subsector identified in the Pollutant Release and Transfer Register—PRTR, that represents the GHG emission sources in mainland Portugal; S.3 Maps obtained from EPIC WebGIS platform (Instituto Superior de Agronomia, Universidade de Lisboa, ISA-UL); S.3.1 Land steepness; S.3.2 Soil texture; S.3.3 Soil pH; S.3.4 Soil thickness; Figure S4. Maps obtained from EPIC WebGIS platform (ISA-UL): (a) Land steepness map measured in %; (b) Soil texture represented as the surface layer up to 30 cm; (c) Soil pH map from soils considered high acid up to very alkaline; (d) Soil thickness in cm; S.3.5 Presence of physical obstacles; S.3.6 Ecological Soil Value; S.3.7 Current Permeability; Figure S5. Maps according to EPIC WebGIS platform (ISA-UL): (a) Map of the presence of physical obstacles; (b) Ecological soil value map; (c) Current permeability; S.3.8 Natural and semi-natural vegetation with conservation value; S.3.9 Soil-morphological aptitude to irrigated agriculture and silviculture; Figure S6. Consulted maps from EPIC WebGIS platform (ISA-UL): (a) Natural and semi-natural vegetation with conservation value map; (b) Soil-morphological aptitude to irrigated agriculture; (c) Soil-morphological aptitude to silviculture; S.4 Consulted maps from Instituto da Conservação da Natureza e das Florestas (ICNF); S.4.1 Protected areas; S.4.2 Unprotected areas; S.4.3 Soil susceptibility to desertification; Figure S7. Maps obtained from ICNF: (a) Protected areas in mainland Portugal corresponding to areas not permitted for cultivation; (b) Unprotected areas, corresponding to zones where cultivation is allowed; (c) Soil susceptibility to desertification; S.5 Maps from Empresa de Desenvolvimento Mineiro (EDM)-Classification of soils according to each mine group (Contaminated soils); S.6 Maps obtained from European Environment Agency (EEA); S.6.1 Wastewater Treatment Plant (WWTP) capacity; S.6.2 Applied treatments to Wastewater Facilities; Figure S8. Maps according to EDM and EEA data: (a) Mines that are being recovered by EDM (contaminated soils); (b) WWTP capacity in mainland Portugal; (c) Treatment applied in WWTPs.

**Author Contributions:** Conceptualization, A.R., P.M. and L.Q.; Methodology, A.R., L.Q., P.P. and P.M.; Validation, A.R., P.M., and L.Q.; Formal analysis, M.A., A.R. and P.P.; Investigation, M.A. and A.R.; Resources, all authors; Data curation, M.A. and A.R.; Writing—Original draft preparation, M.A., A.L.F. and A.R.; Writing—Review and editing, M.A., A.L.F. and A.R.; Supervision, P.M.; Project administration, F.G. and P.M.; Funding acquisition, P.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was integrated in the project CONVERTE, supported by POSEUR (POSEUR-01-1001-FC-000001) under the PORTUGAL 2020 Partnership Agreement. This research has been carried out at the Biomass and Bioenergy Research Infrastructure (BBRI)- LISBOA-01-0145-FEDER-022059, supported by Operational Programme for Competitiveness and Internationalization (PORTUGAL2020), by Lisbon Portugal Regional Operational Programme (Lisboa 2020) and by North Portugal Regional Operational Programme (Norte 2020) under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work has also been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Unit Project Scope: UIDP/04077/2020 and UIDB/04077/2020.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, treatment analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
