Social Impact Scoping Using Statistical Methods: The Case of a Novel Design of Abandoned Farmland Policy

Noriko Irie () and Naoko Kawahara
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Noriko Irie: Faculty of Collaborative Regional Innovation, Ehime University, Ehime 7908577, Japan
Naoko Kawahara: Faculty of Business Administration, Kindai University, Osaka 5778502, Japan

Sustainability, 2023, vol. 15, issue 4, 1-18

Abstract: This study discusses the methodology for social impact scoping (SIS) by employing a case study of novel policy design for resolving the issue of abandoned farmland in Ehime Prefecture, Japan. When conducted by using state-of-the-art methods, SIS can contribute meaningful information for policymaking even in conditions of limited resources. In this study, a choice experiment (CE) was conducted to analyse the desirability of alternative policies for abandoned farmland among local people; additionally, the Bayesian efficient design was employed; this design generally reduces sample size to obtain the statistical significance of the survey results. The increase in abandoned farmland worldwide has been linked to regional, national, and global environmental concerns, such as biodiversity loss and the reduction of landscape diversity; it has also been proven to be a serious problem regarding local sustainability. This study showed that the SIS results can be used to determine measures to prevent farmland abandonment. Overall, the respondents stated that this survey was meaningful for examining measures for abandoned farmland, which suggests the usefulness of implementing SIS by using this type of survey. Thus, this study showed that SIS is a methodology that can pre-screen policies to enhance social well-being even in conditions of limited resources for evaluation and when certain assumptions can be made regarding the choice-based analysis.

Keywords: social impact scoping; choice experiment; Bayesian efficient design; abandoned farmland policy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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