Emissions Trading with Telecommuting Credits: Regulatory Background and Institutional Barriers

The 1999 National Telecommuting and Air Quality Act created pilot programs in five metropolitan areas in the United States to examine whether a particular type of economic incentive, tradable emissions credits created from telecommuting, represents a viable strategy for reducing vehicle miles traveled and improving air quality (H.R. 2094, 2000). Under the ecommute program, companies could generate emissions credits by reducing the vehicle miles traveled (VMT) of their workforce through telework programs. They would then be able to sell the credits to firms that needed the reductions to comply with air quality regulations. This paper provides some context for evaluating whether such a trading scheme represents a feasible approach to reducing mobile source emissions and promoting telecommuting and reviews the limited experience with mobile source emission trading programs. From a regulatory perspective, the most substantial drawback to such a program is its questionable environmental integrity, which is a result of difficulties in establishing sufficiently rigorous quantification protocols to measure accurately the emission reductions from telecommuting. Perhaps more importantly, such a program is not likely to be cost-effective; the emissions reductions from a single telecommuter are extremely small, meaning that any trading program will have relatively high transaction costs to environmental benefits. A comparison of estimated emission reductions from the five pilot cities with historical and projected emission credit and allowance prices indicates that the yearly revenue per participant is likely to be well under $100, substantially below what firms participating in the program said would be an adequate incentive to induce a substantial increase in telecommuting. This discussion paper is the final paper in a series of four on telecommuting published in by RFF in December 2004. In discussion paper 04-42, Walls and Nelson analyze data from five pilot cities enrolled in the “ecommute” program. In 04-43 Safirova and Walls examine the 2002 Telework survey conducted in California and, in 04-44, these authors review the empirical literature on telecommuting with a focus on trip reduction impacts. The studies by RFF are part of a larger report on the ecommute program completed by the Global Environment and Technology Foundation (GETF) for the U.S. Environmental Protection Agency. More information about the overall project can be found on the ecommute/GETF website: http://www.ecommute.net/program/.telecommuting, emissions trading

The Responsibility Quantification (ResQu) Model of Human Interaction with Automation

Intelligent systems and advanced automation are involved in information collection and evaluation, in decision-making and in the implementation of chosen actions. In such systems, human responsibility becomes equivocal. Understanding human casual responsibility is particularly important when intelligent autonomous systems can harm people, as with autonomous vehicles or, most notably, with autonomous weapon systems (AWS). Using Information Theory, we develop a responsibility quantification (ResQu) model of human involvement in intelligent automated systems and demonstrate its applications on decisions regarding AWS. The analysis reveals that human comparative responsibility to outcomes is often low, even when major functions are allocated to the human. Thus, broadly stated policies of keeping humans in the loop and having meaningful human control are misleading and cannot truly direct decisions on how to involve humans in intelligent systems and advanced automation. The current model is an initial step in the complex goal to create a comprehensive responsibility model, that will enable quantification of human causal responsibility. It assumes stationarity, full knowledge regarding the characteristic of the human and automation and ignores temporal aspects. Despite these limitations, it can aid in the analysis of systems designs alternatives and policy decisions regarding human responsibility in intelligent systems and advanced automation

Motion Planning of Uncertain Ordinary Differential Equation Systems

This work presents a novel motion planning framework, rooted in nonlinear programming theory, that treats uncertain fully and under-actuated dynamical systems described by ordinary differential equations. Uncertainty in multibody dynamical systems comes from various sources, such as: system parameters, initial conditions, sensor and actuator noise, and external forcing. Treatment of uncertainty in design is of paramount practical importance because all real-life systems are affected by it, and poor robustness and suboptimal performance result if it’s not accounted for in a given design. In this work uncertainties are modeled using Generalized Polynomial Chaos and are solved quantitatively using a least-square collocation method. The computational efficiency of this approach enables the inclusion of uncertainty statistics in the nonlinear programming optimization process. As such, the proposed framework allows the user to pose, and answer, new design questions related to uncertain dynamical systems. Specifically, the new framework is explained in the context of forward, inverse, and hybrid dynamics formulations. The forward dynamics formulation, applicable to both fully and under-actuated systems, prescribes deterministic actuator inputs which yield uncertain state trajectories. The inverse dynamics formulation is the dual to the forward dynamic, and is only applicable to fully-actuated systems; deterministic state trajectories are prescribed and yield uncertain actuator inputs. The inverse dynamics formulation is more computationally efficient as it requires only algebraic evaluations and completely avoids numerical integration. Finally, the hybrid dynamics formulation is applicable to under-actuated systems where it leverages the benefits of inverse dynamics for actuated joints and forward dynamics for unactuated joints; it prescribes actuated state and unactuated input trajectories which yield uncertain unactuated states and actuated inputs. The benefits of the ability to quantify uncertainty when planning the motion of multibody dynamic systems are illustrated through several case-studies. The resulting designs determine optimal motion plans—subject to deterministic and statistical constraints—for all possible systems within the probability space