The Missing Instrument: Dirty Input Limits
نویسندگان
چکیده
This Article evaluates Dirty Input Limits (“DILs”), quantitative limits on the inputs that cause pollution. An environmental protection instrument that the literature has hitherto largely overlooked, DILs provide an alternative to cumbersome output-based emissions trading and performance standards. DILs have played a role in some of the world’s most prominent environmental success stories. They have also begun to influence climate change policy because of the impossibility of imposing an output-based cap on transport emissions. We evaluate DILs’ administrative advantages, efficiency, dynamic properties, and capacity to better integrate environmental protection efforts. DILs, we show, not only have significant advantages that make them a good policy tool, they also help us to fruitfully reconceptualize environmental law in a more holistic fashion.
منابع مشابه
Cost of Low-Quality Data over Association Rules Discovery
Quality in data mining critically depends on the preparation and on the quality of processed data sets. Indeed data mining processes and applications require various forms of data preparation (and repair) with several data formatting and cleaning techniques, because the data input to the mining algorithms is assumed to conform to nice data distributions, containing no missing, inconsistent or i...
متن کاملDATA MINING , DIRTY DATA , AND COSTS ( Research - in - Progress )
A series of simulations examining the performance of four data mining algorithms in the face of missing data were conducted. The four algorithms were: feed-forward neural networks, logistic regression, C5.0 algorithm, and the Apriori algorithm. A credit card screening data set was used. The original data set was altered by introducing missing data, at increasingly greater levels, and the perfor...
متن کاملApplying Ordinal Association Rules for Cleansing Data With Missing Values
Cleansing data of errors is an important processing step particularly when integrating heterogeneous data sources. Dirty data files are prevalent in data warehouses because of incorrect or missing data values, inconsistent attribute naming conventions or incomplete information. This paper improves the data cleansing ordinal association rules technique by proposing a solution for the missing val...
متن کاملWriting on Dirty Paper in the Presence of Difference Set Noise
Costa’s celebrated “writing on dirty paper” (WDP) shows that the powerconstrained channel Y = X + S + Z, with Gaussian Z, has the same capacity as the standard AWGN channel Y = X + Z, provided that the “interference” S (no matter how strong it is) is known at the transmitter. While this ability for perfect interference cancellation is very appealing, it relies heavily on the Gaussianity of the ...
متن کاملInfluence of Pattern of Missing Data on Performance of Imputation Methods: An Example from National Data on Drug Injection in Prisons
Background Policy makers need models to be able to detect groups at high risk of HIV infection. Incomplete records and dirty data are frequently seen in national data sets. Presence of missing data challenges the practice of model development. Several studies suggested that performance of imputation methods is acceptable when missing rate is moderate. One of the issues which was of less concern...
متن کامل