Building a Composite Indicator to Measure Environmental Sustainability Using Alternative Weighting Methods

Abstract

Environmental sustainability in agriculture can be measured through the construction of composite indicators. However, this is a challenging task because these indexes are heavily dependent on how the individual base indicators are weighted. The main aim of this paper is to contribute to the existing literature regarding the robustness of subjective (based on experts’ opinions) weighting methods when constructing a composite indicator for measuring environmental sustainability at the farm level. In particular, the study analyzes two multi-criteria techniques, the analytic hierarchy process and the recently developed best-worst method, as well as the more straightforward point allocation method. These alternative methods have been implemented to empirically assess the environmental performance of irrigated olive farms in Spain. Data for this case study were collected from a panel of 22 experts and a survey of 99 farms. The results obtained suggest that there are no statistically significant differences in the weights of the individual base indicators derived from the three weighting methods considered. Moreover, the ranking of the sampled farms, in terms of their level of environmental sustainability measured through the composite indicators proposed, is not dependent on the use of the different weighting methods. Thus, the results support the robustness of the three weighting methods considered.

Ranking and Classification of Cryptocurrency Exchanges Using the Methods of a Multidimensional Comparative Analysis

“…In the analysed case six weighting schemes were considered: arbitrarily selected weights, equal weights, weights obtained with Papenbrock's method, weights based on coefficients of a variation of diagnostic variables, weights based on the importance of diagnostic variables and taxonomic measures obtained with a simulation method. The existing literature offers also many advanced methods for determining the weights of diagnostic variables, such as: PCA, mathematical programming, sensitivity analysis, multiple linear regression (Zhou, Ang, Poh, 2007;Becker, Saisana, Paruolo, Vandercasteele, 2017;Gan et al, 2017;Greco, Ishizka, Tasiou, Torrisi, 2019;Kuc-Czarnecka, 2019;Gomez-Limon, Arrizza, Guerrero-Baena, 2020). Each of the weighting schemes has its benefits and drawbacks.…”

Assessing the Climate Resilience of Sub-Saharan Africa (SSA): A Metric-Based Approach

“…This study aims to evaluate and compare the resilience of countries in Sub-Saharan Africa (SSA) to the negative impacts of climate change through the use of composite index measurement [34,35,46] and vulnerability and readiness index metric [43]. The assessment of the climate resilience of the countries in SSA consist of variables that help to either increase or decrease the risk of adverse climate-related disasters.…”

“…Single indicator variables cannot directly measure a phenomenon; therefore, we created a composed index by combining many variables that could be effective for decisionmaking and comparing a country's performance. Many researchers [34,35,46] have used a wide range of indicator variables in their attempt to measure disaster resilience. These indicator variables represent different dimensions of resilience, including social, economic, environmental, infrastructural, institutional, community, ecological, physical, etc.…”

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Investigating Eco-Environmental Vulnerability for China–Pakistan Economic Corridor Key Sector Punjab Using Multi-Sources Geo-Information

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