An improvement selection methodology for key performance indicators

Key performance indicators (KPIs) are critical measures for determining the health of a manufacturing plant in relationship to the plant’s goals. In today’s competitive environment, manufacturers cannot be careless about their business; in fact, they must ensure that their KPIs are effective and use them to make improvements when necessary. This paper describes a method for suggesting improvements to a manufacturer’s KPIs, based on the results achieved from a workshop to score the KPI on a number of predefined criteria. The approach uses a prospect theory approach to weight the scoring. Different problem formulations were derived that allow for both recommendations for improvements and the recommendations for disinvestments to over-performing KPIs. The authors applied the developed approach to two workshop outputs, each from independent manufacturers, and the results highlighted the significant difference between the two manufacturers in terms of improvement priorities and KPI assessment. The optimal improvement suggestions were compared to those found through a fast heuristic. It was determined that given the underlying assumptions of the approach that the heuristic solutions were just as adequate as the optimal ones.     

Key factors that influence households’ tree planting behaviour

Despite a decrease in indigenous forests and a growing demand for tree products in developing countries, tree planting activities are not considerably expanding in Tanzania. In this paper, we analyse factors that influence households’ tree planting behaviour, as well as the number of trees planted. Coast and Morogoro regions in the east of Tanzania were selected as the case, and data was gathered from 202 households in 11 villages in these regions where tree planting programmes have been or still are active. A Heckman model is used to analyse the factors that drive tree planting behaviour. Results indicate that households get wood energy from forest reserves (57%), in addition to their own planted trees (9.1%). Emperical findings show that the most important factors have significantly positive effects on households’ tree planting behaviour, as well as the extent to which it was implemented. These factors include households’ land sizes, households’ awareness of tree planting programmes, tree planting for wood energy, and the age of the head of the household. The right/freedom to harvest and transport tree products, households’ attitudes towards tree planting, and family size have significantly negative effects on households’ tree planting behaviour. This paper is perhaps the first comprehensive study to analyse the factors that influence households’ tree planting behaviour in Tanzania, and it uncovers results that are useful, even for other developing countries with similar conditions.     

Carbon emission reduction and cost–benefit of methane digester systems on hog farms in China

Three different sizes of hog farms were selected to analyze the carbon emissions reduction and the cost–benefit of three methane digester systems. The sizes of the digesters are 2,200, 2,200 and 800 m³, respectively. The sales of slaughter hogs from them are 50,000, 35,000 and 10,000 head, respectively. The carbon emissions reductions were 5,237, 4,017, and 1,334 tons, respectively. The results show that while the methane digester systems have a significant effect on carbon emissions reduction, it is difficult to operate the systems sustainably. If the carbon emissions reduction can be traded at high enough prices in the carbon offset markets, then the systems will be profitable and sustainable. Newly established China's domestic carbon offset market could provide this possibility, but more government support is needed. In addition, this study shows that scale economies make the digester adoption relatively more profitable for larger farms than smaller ones.     

Are national biodiversity strategies and action plans appropriate for building responsibilities for mainstreaming biodiversity across policy sectors? The case of Finland

The Convention on Biological Diversity's national biodiversity strategies and action plans (NBSAPs) are major mechanisms for mainstreaming biodiversity into national policies. This article examines whether and how the NBSAPs contribute to mainstreaming biodiversity across policy sectors in Finland in order to halt biodiversity loss. We have developed an innovative analytical framework where the concept of responsibility addresses how motivations for mainstreaming can be built, and the concept of social learning outcomes addresses the extent of institutional changes for biodiversity. The Finnish NBSAP processes have been able to build diverse forms of responsibility (liability, accountability, responsiveness and care) in different policy sectors by providing new knowledge, careful process design and developing institutional linkages. Despite pro-biodiversity outcomes in the targeted policy sectors, the responsibilities do not diffuse from the environmental administration to other policy sectors to a sufficient extent. Closing this ‘responsibility gap’ is a key challenge for building effective environmental policies.     

Characterizing Drought in Irrigated Agricultural Systems: The Surface Water Delivery Index (SWDI)

Quantifying surface water shortages in arid and semiarid agricultural regions is challenging because limited water supplies are distributed over long distances based on complex water management systems constrained by legal, economic, and social frameworks that evolve with time. In such regions, the water supply is often derived in a climate dramatically different from where the water is diverted to meet agricultural demand. The existing drought indices which rely on local climate do not portray the complexities of the economic and legal constraints on water delivery. Nor do these indices quantify the shortages that occur in drought. Therefore, this research proposes a methodological approach to define surface water shortages in irrigated agricultural systems using a newly developed index termed the Surface Water Delivery Index (SWDI). The SWDI can be used to uniformly quantify surface water deficits/shortages at the end of the irrigation season. Results from the SWDI clearly illustrate how water shortages in droughts identified by the existing indices (e.g., SPI and PDSI) vary strongly both within and between basins. Some surface water entities are much more prone to water shortages than other entities based both on their source of water supply and water right portfolios.     

Augmenting Watershed Model Calibration with Incorporation of Ancillary Data Sources and Qualitative Soft Data Sources

Watershed simulation models such as the Soil & Water Assessment Tool (SWAT) can be calibrated using “hard data” such as temporal streamflow observations; however, users may find upon examination of model outputs, that the calibrated models may not reflect actual watershed behavior. Thus, it is often advantageous to use “soft data” (i.e., qualitative knowledge such as expected denitrification rates that observed time series do not typically exist) to ensure that the calibrated model is representative of the real world. The primary objective of this study is to evaluate the efficacy of coupling SWAT‐Check (a post‐evaluation framework for SWAT outputs) and IPEAT‐SD (Integrated Parameter Estimation and Uncertainty Analysis Tool‐Soft & hard Data evaluation) to constrain the bounds of soft data during SWAT auto‐calibration. IPEAT‐SD integrates 59 soft data variables to ensure SWAT does not violate physical processes known to occur in watersheds. IPEAT‐SD was evaluated for two case studies where soft data such as denitrification rate, nitrate attributed from subsurface flow to total discharge ratio, and total sediment loading were used to conduct model calibration. Results indicated that SWAT model outputs may not satisfy reasonable soft data responses without providing pre‐defined bounds. IPEAT‐SD provides an efficient and rigorous framework for users to conduct future studies while considering both soft data and traditional hard information measures in watershed modeling.