تاثیر سیستم حسابداری محیطی بر سیستم های اطلاعات برای تصمیم گیری
Abstract
Keywords
1- Introduction
2- Guatemala case study
3- Netherlands case study
4- Water case study
5. Conclusions
Acknowledgements
References
Abstract
The System of Environmental-Economic Accounting (SEEA) is a framework integrating information from different sources with the aim of enabling better decision making by governments, business and others. Accounting allows a wide variety of data to be synthesised so that regular information and indicators are produced and can feed into decision-making processes. The accounting recognises that while there may be discrepancies between different data sources as well as data gaps, government and business must continually make decisions. Over time both the accounts and underlying data improves across the six dimensions of data quality – relevance, accuracy, timeliness, accessibility, interpretability and coherence. In individual data sources the focus is mostly on accuracy (i.e. closeness of estimate to the real number) but accounting addresses all of the six dimensions and has particular strengths in timeliness, accessibility, interpretability and coherence providing data when it is needed in a consistent format. Using examples from high and low-income countries we describe how SEEA can improve information systems and data quality for decision making and distil lessons for the development of the European Shared Environmental Information System.
Introduction
The objective of this paper is to outline how better decisions aimed at balancing human and environmental needs can be enabled by having more regular, consistent and integrated environmental and economic information via accounting. In doing this the basic aspects of data quality are described, along with international accounting frameworks for organising information and how these have been applied in three case studies. The collection, arrangement and availability of data is key to evidenced-based public policy (e.g. Banks, 2008; Head, 2010). Describing and understanding the quality of data being used in decision making is important in science (e.g. Manning et al., 2004; Regan et al., 2005), government (e.g. Vardon, 2013) and business (e.g. Samitsch, 2015). Accepting there is always uncertainty in decision making due to the quality of the data and imperfect understanding of the system(s) that the data describes is an important first step for data providers. Governments, business and others make decisions using the information available and also make assumptions about both future human behaviour (e.g. response to new taxes or subsidies) and the environmental factors (e.g. the weather). Uncertainty in data and imperfect understanding of systems can be reduced through a combination of theoretical and practical measures., which in turn enables better decisions to be made.