Land suitability assessment for Paulownia cultivation using combined GIS and Z-number DEA: A case study

Abstract

Paulownia has emerged as a prospering biomass resource for the production of renewable energy. Many countries have decided to cultivate this tree in order to reduce air pollution and secure the increasing energy demand. In this paper, a hybrid approach, including geographical information system (GIS) and mathematical modeling is proposed to determine suitable locations for Paulownia cultivation. The feasibility and suitability maps for Paulownia cultivation have been derived based on three categories of criteria, including (1) Certain Non-Compensatory Criteria (CNCC) (2) Uncertain Compensatory Criteria (UCC), and (3) Certain Compensatory Criteria (CCC), and a four-stage algorithm is proposed. This algorithm sets out to (1) recognize Paulownia growth conditions (2) identify feasible regions considering CNCC (3) evaluate the efficiency of candidate locations by employing data envelopment analysis (DEA) with respect to UCC, and (4) display suitability map according to CCC. The high degree of fluctuation in weather conditions is an important factor that significantly influences the DEA results. Therefore, Z-number is adopted to represent the uncertainty of input data in DEA modeling. To validate the proposed approach, the obtained efficiencies from Z-number, fuzzy number, and crisp DEA models are compared to those obtained from the actual data of a five years period. The results indicate that about 160,000 km2 land area is suitable to cultivate Paulownia in Iran that shows great potential for renewable energy production in this country. On the other hand, the computational results demonstrate that applying Z-number decreases the error of the weather forecast by 8% so that land suitability assessment can be carried out more accurately. As a result, this reliable method can prevent agricultural productivity loss due to selecting inappropriate locations.