Non-Financial Returns of Enterprise-Led Development Assistance - A Study of Energy-Related Enterprises

The Rural Energy Enterprise Development (REED) initiative provides assistance to energy-related enterprises to prepare them for growth and to make eventual investments by mainstream financial partners less risky. This study assesses the non-financial returns of a number of REED-type enterprises and provides guidance for the selection and ongoing evaluation of these enterprises within the context of development interventions. This study concludes that desired development outcomes should provide the basis for programme objectives against which non-financial returns can be measured. However, qualitative information is also necessary, as the context within which an enterprise operates largely defines the importance of these returns. Further work to improve the understanding of this context is necessary prior to the development of a formalised monitoring programme. Limitations of the assessment process should be recorded and addressed through the ongoing review of the programme, other monitoring efforts and further research

The Use of National Pollutant Inventory Data on Atmospheric Dispersion Modelling

Atmospheric dispersion modelling can be used to estimate the environmental impact of releases to air. The purpose of this paper is determine whether the National Pollutant Inventory (NPI), Australia's national database of pollutant releases, can be used for atmospheric dispersion modelling and, if so, the conditions that must be satisfied for it to be used effectively. The selection of emission estimation techniques (EETs) significantly affects the reliability of reported NPI emissions. The reliability of NPI data has improved as facilities gain a better understanding of the reporting process, as Industry Handbooks are reviewed and as facilities find beneficial uses for NPI data within their organisations. Although NPI data in isolation do not satisfy dispersion modelling requirements, it is likely that the necessary supporting information relating to variation in emissions and source characteristics will be obtained or calculated by industry as part of the NPI reporting process. Regulatory authorities may be able to obtain these data through other regulatory requirements. To lessen the burden on industry in collecting the necessary supporting information for regional dispersion modelling, environmental regulatory authorities could consider the coordinated collection, storage and updating of the necessary information. Dispersion modelling using NPI data may be associated with relatively large uncertainties. However, provided that the uncertainty in NPI emissions estimates is recognised and depending on the end use of the modelling, predictions based on these data could provide the basis for effective decision makin

Improving Cleaner Production through Pollutant Release and Transfer Register Reporting Processes

An effective cleaner production programme requires the efficient collection and interpretation of data relating to a facility's activities, including information of pollutant emissions, wastes generated and raw materials consumed. Many of these data are required to be collected and reported under national Pollutant Release and Transfers Registers (PRTRs) which are an increasingly common environmental policy tool in the OECD countries. This article considers the relationship between cleaner production and PRTR reporting, and discusses whether cleaner production programmes can be enhanced by the use of PRTR data. A case-study of a fertiliser manufacturing facility reporting under the Australian National Pollutant Inventory (NPI) is used to analyse and illustrate the practical issues around linking cleaner production with PRTR reporting. PRTR reporting can assist in identifying cleaner production opportunities and in generating the data sets required to design, implement and monitor cleaner production programmes. There are, however, some limitations in PRTR data that need to be recognised. Specifically, these are issues around data quality and comparability (e.g. PRTRs rely heavily on default emissions factors rather than the monitoring data that are generally required for cleaner production programmes) and the different data sets required for PRTR reporting (which generally require reporting at the site level rather than at the individual process unit level that is required for cleaner production)